a double blind experiment is when

Double-Blind Experimental Study And Procedure Explained

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What is a Blinded Study?

• Binding, or masking, refers to withholding information regarding treatment allocation from one or more participants in a clinical research study, typically in randomized control trials .
• A blinded study prevents the participants from knowing about their treatment to avoid bias in the research. Any information that can influence the subjects is withheld until the completion of the research.
• Blinding can be imposed on any participant in an experiment, including researchers, data collectors, evaluators, technicians, and data analysts. 
• Good blinding can eliminate experimental biases arising from the subjects’ expectations, observer bias, confirmation bias, researcher bias, observer’s effect on the participants, and other biases that may occur in a research test.
• Studies may use single-, double- or triple-blinding. A trial that is not blinded is called an open trial.

Double-Blind Studies

Double-blind studies are those in which neither the participants nor the experimenters know who is receiving a particular treatment.

Double blinding prevents bias in research results, specifically due to demand characteristics or the placebo effect.

Demand characteristics are subtle cues from researchers that can inform the participants of what the experimenter expects to find or how participants are expected to behave.

If participants know which group they are assigned to, they might change their behavior in a way that would influence the results. Similarly, if a researcher knows which group a participant is assigned to, they might act in a way that reveals the assignment or influences the results.

Double-blinding attempts to prevent these risks, ensuring that any difference(s) between the groups can be attributed to the treatment. 

On the other hand, single-blind studies are those in which the experimenters are aware of which participants are receiving the treatment while the participants are unaware.

Single-blind studies are beneficial because they reduce the risk of errors due to subject expectations. However, single-blind studies do not prevent observer bias, confirmation bias , or bias due to demand characteristics.

Because the experiments are aware of which participants are receiving which treatments, they are more likely to reveal subtle clues that can accidentally influence the research outcome.

Double-blind studies are considered the gold standard in research because they help to control for experimental biases arising from the subjects’ expectations and experimenter biases that emerge when the researchers unknowingly influence how the subjects respond or how the data is collected.

Using the double-blind method improves the credibility and validity of a study .

Example Double-Blind Studies

Rostock and Huber (2014) used a randomized, placebo-controlled, double-blind study to investigate the immunological effects of mistletoe extract. However, their study showed that double-blinding is impossible when the investigated therapy has obvious side effects. 

Using a double-blind study, Kobak et al. (2005) found that S t John’s wort ( Hypericum perforatum ) is not an efficacious treatment for anxiety disorder, specifically OCD.

Using the Yale–Brown Obsessive–Compulsive Scale (Y-BOCS), they found that the mean change with St John’s wort was not significantly different from the mean change found with placebo. 

Cakir et al. (2014) conducted a randomized, controlled, and double-blind study to test the efficacy of therapeutic ultrasound for managing knee osteoarthritis.

They found that all assessment parameters significantly improved in all groups without a significant difference, suggesting that therapeutic ultrasound provided no additional benefit in improving pain and functions in addition to exercise training.

Using a randomized double-blind study, Papachristofilou et al. (2021) found that whole-lung LDRT failed to improve clinical outcomes in critically ill patients admitted to the intensive care unit requiring mechanical ventilation for COVID-19 pneumonia.

Double-Blinding Procedure

Double blinding is typically used in clinical research studies or clinical trials to test the safety and efficacy of various biomedical and behavioral interventions.

In such studies, researchers tend to use a placebo. A placebo is an inactive substance, typically a sugar pill, that is designed to look like the drug or treatment being tested but has no effect on the individual taking it. 

The placebo pill was given to the participants who were randomly assigned to the control group. This group serves as a baseline to determine if exposure to the treatment had any significant effects.

Those randomly assigned to the experimental group are given the actual treatment in question. Data is collected from both groups and then compared to determine if the treatment had any impact on the dependent variable.

All participants in the study will take a pill or receive a treatment, but only some of them will receive the real treatment under investigation while the rest of the subjects will receive a placebo. 

With double blinding, neither the participants nor the experimenters will have any idea who receives the real drug and who receives the placebo. 

For Example

A common example of double-blinding is clinical studies that are conducted to test new drugs.

In these studies, researchers will use random assignment to allocate patients into one of three groups: the treatment/experimental group (which receives the drug), the placebo group (which receives an inactive substance that looks identical to the treatment but has no drug in it), and the control group (which receives no treatment).

Both participants and researchers are kept unaware of which participants are allocated to which of the three groups.

The effects of the drug are measured by recording any symptoms noticed in the patients.

Once the study is unblinded, and the researchers and participants are made aware of who is in which group, the data can be analyzed to determine whether the drug had effects that were not seen in the placebo or control group, but only in the experimental group. 

Double-blind studies can also be beneficial in nonmedical interventions, such as psychotherapIes.

Reduces risk of bias

Double-blinding can eliminate, or significantly reduce, both observer bias and participant biases.

Because both the researcher and the subjects are unaware of the treatment assignments, it is difficult for their expectations or behaviors to influence the study.

Results can be duplicated

The results of a double-blind study can be duplicated, enabling other researchers to follow the same processes, apply the same test item, and compare their results with the control group.

If the results are similar, then it adds more validity to the ability of a medication or treatment to provide benefits. 

It tests for three groups

Double-blind studies usually involve three groups of subjects: the treatment group, the placebo group, and the control group.

The treatment and placebo groups are both given the test item, although the researcher does not know which group is getting real treatment or placebo treatment.

The control group doesn’t receive anything because it serves as the baseline against which the other two groups are compared.

This is an advantage because if subjects in the placebo group improved more than the subjects in the control group, then researchers can conclude that the treatment administered worked.

Applicable across multiple industries

Double-blind studies can be used across multiple industries, such as agriculture, biology, chemistry, engineering, and social sciences.

Double-blind studies are used primarily by the pharmaceutical industry because researchers can look directly at the impact of medications. 

Disadvantages

Inability to blind.

In some types of research, specifically therapeutic, the treatment cannot always be disguised from the participant or the experimenter. In these cases, you must rely on other methods to reduce bias.

Additionally, imposing blinding may be impossible or unethical for some studies. 

Double-blinding can be expensive because the researcher has to examine all the possible variables and may have to use different groups to gather enough data. 

Small Sample Size

Most double-blind studies are too small to provide a representative sample. To be effective, it is generally recommended that double-blind trials include around 100-300 participants.

Studies involving fewer than 30 participants generally can’t provide proof of a theory. 

Negative Reaction to Placebo

In some instances, participants can have adverse reactions to the placebo, even producing unwanted side effects as if they were taking a real medication. 

It doesn’t reflect real-life circumstances

When participants receive treatment or medication in a double-blind placebo study, each individual is told that the item in question might be real medication or a placebo.

This artificial situation does not represent real-life circumstances because when a patient receives a pill after going to the doctor in the real-world, they are told that the product is actual medicine intended to benefit them.

When situations don’t feel realistic to a participant, then the quality of the data can decrease exponentially.

What is the difference between a single-blind, double-blind, and triple-blind study?

In a single-blind study, the experimenters are aware of which participants are receiving the treatment while the participants are unaware.

In a double-blind study, neither the patients nor the researchers know which study group the patients are in. In a triple-blind study, neither the patients, clinicians, nor the people carrying out the statistical analysis know which treatment the subjects had.

Is a double-blind study the same as a randomized clinical trial?

Yes, a double-blind study is a form of a randomized clinical trial in which neither the participants nor the researcher know if a subject is receiving the experimental treatment, a standard treatment, or a placebo.

Are double-blind studies ethical?

Double blinding is ethical only if it serves a scientific purpose. In most circumstances, it is unethical to conduct a double-blind placebo controlled trial where standard therapy exists.

What is the purpose of randomization using double blinding?

Randomization with blinding avoids reporting bias, since no one knows who is being treated and who is not, and thus all treatment groups should be treated the same. This reduces the influence of confounding variables and improves the reliability of clinical trial results.

Why are double-blind experiments considered the gold standard?

Randomized double-blind placebo control studies are considered the “gold standard” of epidemiologic studies as they provide the strongest possible evidence of causality.

Additionally, because neither the participants nor the researchers know who has received what treatment, double-blind studies minimize the placebo effect and significantly reduce bias.

Can blinding be used in qualitative studies?

Yes, blinding is used in qualitative studies .

Cakir, S., Hepguler, S., Ozturk, C., Korkmaz, M., Isleten, B., & Atamaz, F. C. (2014). Efficacy of therapeutic ultrasound for the management of knee osteoarthritis: a randomized, controlled, and double-blind study. American journal of physical medicine & rehabilitation , 93 (5), 405-412.

Kobak, K. A., Taylor, L. V., Bystritsky, A., Kohlenberg, C. J., Greist, J. H., Tucker, P., … & Vapnik, T. (2005). St John’s wort versus placebo in obsessive–compulsive disorder: results from a double-blind study. International Clinical Psychopharmacology , 20 (6), 299-304.

Papachristofilou, A., Finazzi, T., Blum, A., Zehnder, T., Zellweger, N., Lustenberger, J., … & Siegemund, M. (2021). Low-dose radiation therapy for severe COVID-19 pneumonia: a randomized double-blind study. International Journal of Radiation Oncology* Biology* Physics , 110 (5), 1274-1282. Rostock, M., & Huber, R. (2004). Randomized and double-blind studies–demands and reality as demonstrated by two examples of mistletoe research. Complementary Medicine Research , 11 (Suppl. 1), 18-22.

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Double-Blind Studies in Research

A double-blind study is one in which neither the participants nor the experimenters know who is receiving a particular treatment. This procedure is utilized to prevent bias in research results. Double-blind studies are particularly useful for preventing bias due to demand characteristics or the placebo effect .

For example, let's imagine that researchers are investigating the effects of a new drug. In a double-blind study, the researchers who interact with the participants would not know who was receiving the actual drug and who was receiving a placebo.

A Closer Look at Double-Blind Studies

Let’s take a closer look at what we mean by a double-blind study and how this type of procedure works. As mentioned previously, double-blind indicates that the participants and the experimenters are unaware of who is receiving the real treatment. What exactly do we mean by ‘treatment'? In a psychology experiment, the treatment is the level of the independent variable that the experimenters are manipulating.

This can be contrasted with a single-blind study in which the experimenters are aware of which participants are receiving the treatment while the participants remain unaware.

In such studies, researchers may use what is known as a placebo. A placebo is an inert substance, such as a sugar pill, that has no effect on the individual taking it. The placebo pill is given to participants who are randomly assigned to the control group. A control group is a subset of participants who are not exposed to any levels of the independent variable . This group serves as a baseline to determine if exposure to the independent variable had any significant effects.

Those randomly assigned to the experimental group are given the treatment in question. Data collected from both groups are then compared to determine if the treatment had some impact on the dependent variable .

All participants in the study will take a pill, but only some of them will receive the real drug under investigation. The rest of the subjects will receive an inactive placebo. With a double-blind study, the participants and the experimenters have no idea who is receiving the real drug and who is receiving the sugar pill.

Double-blind experiments are simply not possible in some scenarios. For example, in an experiment looking at which type of psychotherapy is the most effective, it would be impossible to keep participants in the dark about whether or not they actually received therapy.

Reasons to Use a Double-Blind Study

So why would researchers opt for such a procedure? There are a couple of important reasons.

• First, since the participants do not know which group they are in, their beliefs about the treatment are less likely to influence the outcome.
• Second, since researchers are unaware of which subjects are receiving the real treatment, they are less likely to accidentally reveal subtle clues that might influence the outcome of the research.  

The double-blind procedure helps minimize the possible effects of experimenter bias.   Such biases often involve the researchers unknowingly influencing the results during the administration or data collection stages of the experiment. Researchers sometimes have subjective feelings and biases that might have an influence on how the subjects respond or how the data is collected.

In one research article, randomized double-blind placebo studies were identified as the "gold standard" when it comes to intervention-based studies.   One of the reasons for this is the fact that random assignment reduces the influence of confounding variables.

Imagine that researchers want to determine if consuming energy bars before a demanding athletic event leads to an improvement in performance. The researchers might begin by forming a pool of participants that are fairly equivalent regarding athletic ability. Some participants are randomly assigned to a control group while others are randomly assigned to the experimental group.

Participants are then be asked to eat an energy bar. All of the bars are packaged the same, but some are sports bars while others are simply bar-shaped brownies. The real energy bars contain high levels of protein and vitamins, while the placebo bars do not.

Because this is a double-blind study, neither the participants nor the experimenters know who is consuming the real energy bars and who is consuming the placebo bars.

The participants then complete a predetermined athletic task, and researchers collect data performance. Once all the data has been obtained, researchers can then compare the results of each group and determine if the independent variable had any impact on the dependent variable.  

A Word From Verywell

A double-blind study can be a useful research tool in psychology and other scientific areas. By keeping both the experimenters and the participants blind, bias is less likely to influence the results of the experiment. 

A double-blind experiment can be set up when the lead experimenter sets up the study but then has a colleague (such as a graduate student) collect the data from participants. The type of study that researchers decide to use, however, may depend upon a variety of factors, including characteristics of the situation, the participants, and the nature of the hypothesis under examination.

National Institutes of Health. FAQs About Clinical Studies .

Misra S. Randomized double blind placebo control studies, the "Gold Standard" in intervention based studies . Indian J Sex Transm Dis AIDS . 2012;33(2):131-4. doi:10.4103/2589-0557.102130

Goodwin, CJ. Research In Psychology: Methods and Design . New York: John Wiley & Sons; 2010.

Kalat, JW. Introduction to Psychology . Boston, MA: Cengage Learning; 2017.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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• What Is a Double-Blind Study? | Introduction & Examples

What Is a Double-Blind Study? | Introduction & Examples

Published on 6 May 2022 by Lauren Thomas . Revised on 17 October 2022.

In experimental research , subjects are randomly assigned to either a treatment or control group . A double-blind study withholds each subject’s group assignment from both the participant and the researcher performing the experiment.

If participants know which group they are assigned to, there is a risk that they might change their behaviour in a way that would influence the results. If researchers know which group a participant is assigned to, they might act in a way that reveals the assignment or directly influences the results.

Double blinding guards against these risks, ensuring that any difference between the groups can be attributed to the treatment.

Table of contents

Different types of blinding, importance of blinding, frequently asked questions about double-blind studies.

Blinding means withholding which group each participant has been assigned to. Studies may use single, double or triple blinding.

Single blinding occurs in many different kinds of studies, but double and triple blinding are mainly used in medical research.

Single blinding

If participants know whether they were assigned to the treatment or control group, they might modify their behaviour as a result, potentially changing their eventual outcome.

In a single-blind experiment, participants do not know which group they have been placed in until after the experiment has finished.

If participants in the control group realise they have received a fake vaccine and are not protected against the flu, they might modify their behaviour in ways that lower their chances of becoming sick – frequently washing their hands, avoiding crowded areas, etc. This behaviour could narrow the gap in sickness rates between the control group and the treatment group, thus making the vaccine seem less effective than it really is.

Double blinding

When the researchers administering the experimental treatment are aware of each participant’s group assignment, they may inadvertently treat those in the control group differently from those in the treatment group. This could reveal to participants their group assignment, or even directly influence the outcome itself.

In double-blind experiments, the group assignment is hidden from both the participant and the person administering the experiment.

If these experimenters knew which vaccines were real and which were fake, they might accidentally reveal this information to the participants, thus influencing their behaviour and indirectly the results.

They could even directly influence the results. For instance, if experimenters expect the vaccine to result in lower levels of flu symptoms, they might accidentally measure symptoms incorrectly, thus making the vaccine appear more effective than it really is.

Triple blinding

Although rarely implemented, triple-blind studies occur when group assignment is hidden not only from participants and administrators, but also from those tasked with analysing the data after the experiment has concluded.

Researchers may expect a certain outcome and analyse the data in different ways until they arrive at the outcome they expected, even if it is merely a result of chance.

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Blinding helps ensure a study’s internal validity , or the extent to which you can be confident any link you find in your study is a true cause-and-effect relationship.

Since non-blinded studies can result in participants modifying their behaviour or researchers finding effects that do not really exist, blinding is an important tool to avoid bias in all types of scientific research.

Risk of unblinding

Unblinding occurs when researchers have blinded participants or experimenters, but they become aware of who received which treatment before the experiment has ended.

This may result in the same outcomes as would have occurred without any blinding.

You randomly assign some students to the new programme (the treatment group), while others are instructed with a standard programme (the control group). You use single blinding: you do not inform students whether they are receiving the new instruction programme or the standard one.

If students become aware of which programme they have been assigned to – for example, by talking to previous students about the content of the programme – they may change their behaviour. Students in the control group might work harder on their reading skills to make up for not receiving the new programme, or conversely to put in less effort instead since they might believe the other students will do better than them anyway.

Inability to blind

Double or triple blinding is often not possible. While medical experiments can usually use a placebo or fake treatment for blinding, in other types of research, the treatment sometimes cannot be disguised from either the participant or the experimenter. For example, many treatments that physical therapists perform cannot be faked.

In such cases, you must rely on other methods to reduce bias.

• Running a single- rather than double- or triple-blind study. Sometimes, although you might not be able to hide what each subject receives, you can still prevent them from knowing whether they are in the treatment or control group. Single blinding is particularly useful in non-medical studies where you cannot use a placebo in the control group.
• Relying on objective measures that participants and experimenters have less control over rather than subjective ones, like measuring fever rather than self-reported pain. This should reduce the possibility that participants or experimenters could influence the results.
• Pre-registering data analysis techniques. This will prevent researchers from trying different measures of analysis until they arrive at the answer they’re expecting.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

Blinding is important to reduce bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behaviour in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analysing the data.

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Double Blind Study – Blinded Experiments

In science and medicine, a blind study or blind experiment is one in which information about the study is withheld from the participants until the experiment ends. The purpose of blinding an experiment is reducing bias, which is a type of error . Sometimes blinding is impractical or unethical, but in many experiments it improves the validity of results. Here is a look at the types of blinding and potentials problems that arise.

Single Blind, Double Blind, and Triple Blind Studies

The three types of blinding are single blinding, double blinding, and triple blinding:

Single Blind Study

In a single blind study , the researchers and analysis team know who gets a treatment, but the experimental subjects do not. In other words, the people performing the study know what the independent variable is and how it is being tested. The subjects are unaware whether they are receiving a placebo or a treatment. They may even be unaware what, exactly, is being studied.

Example: Violin Study

For example, consider an experiment that tests whether or not violinists can tell the difference a Stradivarius violin (generally regarded as superior) and a modern violin. The researchers know the type of violin they hand to a violinist, but the musician does not (is blind). In case you’re curious, in an actual experiment performed by Claudia Fritz and Joseph Curtin, it turned out violinists actually can’t tell the instruments apart.

Double Blind Study

In a double blind study, neither the researchers nor subjects know which group receives a treatment and which gets a placebo .

Example: Drug Trial

Many drug trials are double-blinded, where neither the doctor nor patient knows whether the drug or a placebo is administered. So, who gets the drug or the placebo is randomly assigned (without the doctor knowing who gets what). The inactive ingredients, color, and size of a pill (for example) are the same whether it is the treatment or placebo.

Triple Blind Study

A triple blind study includes an additional level of blinding. So, the data analysis team or the group overseeing an experiment is blind, in addition to the researchers and subjects.

Example: Vaccine Study

Triple blind studies are common as part of the vaccine approval process. Here, the people who analyze vaccine effectiveness collate data from many test sites and are unaware of which group a participant belongs to.

Some guidelines advocate for removing terms like “single blind” and “double blind” because they do not inherently describe which party is blinded. For example, a double blind study could mean the subjects and scientists are blind or it could mean the subjects and assessors are blind. When you describe blinding in an experiment, report who is blinded and what information is concealed.

The point of blinding is minimizing bias. Subjects have expectations if they know they receive a placebo versus a treatment. And, researchers have expectations regarding the expected outcome. For example, confirmation bias occurs when an investigator favors outcomes that support pre-existing research or the scientist’s own beliefs.

Unblinding is when masked information becomes available. In experiments with humans, intentional unblinding after a study concludes is typical. This way, a subject knows whether or not they received a treatment or placebo. Unblinding after a study concludes does not introduce bias because the data has already been collected and analyzed.

However, premature unblinding also occurs. For example, a doctor reviewing bloodwork often figures out who is getting a treatment and who is getting a placebo. Similarly, patients feeling an effect from a pill or injection suspect they are in the treatment group. One safeguard against this is an active placebo. An active placebo causes side effects, so it’s harder to tell treatment and placebo groups apart just based on how a patient feels.

Although premature unblinding affects the outcome of the results, it isn’t usually reported. This is a problem because unintentional unblinding favors false positives, at least in medicine. For example, if subjects believe they are receiving treatment, they often feel better even if a therapy isn’t effective. Premature unblinding is one of the issues at the heart of the debate about whether or not antidepressants are effective. But, it applies to all blind studies.

Uses of Blind Studies

Of course, blind studies are valuable in medicine and scientific research. But, they also have other applications.

For example, in a police lineup, having an officer familiar with the suspects can influence a witness’s selection. A better option is a blind procedure, using an office who does not know a suspect’s identity. Product developers routinely use blind studies for determining consumer preference. Orchestras use blind judging for auditions. Some employers and educational institutions use blind data for application selection.

• Bello, Segun; Moustgaard, Helene; Hróbjartsson, Asbjørn (October 2014). “The risk of unblinding was infrequently and incompletely reported in 300 randomized clinical trial publications”. Journal of Clinical Epidemiology . 67 (10): 1059–1069. doi: 10.1016/j.jclinepi.2014.05.007
• Daston, L. (2005). “Scientific Error and the Ethos of Belief”. Social Research . 72 (1): 18. doi: 10.1353/sor.2005.0016
• MacCoun, Robert; Perlmutter, Saul (2015). “Blind analysis: Hide results to seek the truth”. Nature . 526 (7572): 187–189. doi: 10.1038/526187a
• Moncrieff, Joanna; Wessely, Simon; Hardy, Rebecca (2018). “Meta-analysis of trials comparing antidepressants with active placebos”. British Journal of Psychiatry . 172 (3): 227–231. doi: 10.1192/bjp.172.3.227
• Schulz, Kenneth F.; Grimes, David A. (2002). “Blinding in randomised trials: hiding who got what”. Lancet . 359 (9307): 696–700. doi: 10.1016/S0140-6736(02)07816-9

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Double Blind Study (Definition + Examples)

The impact of many treatments can only be confirmed after their effect has been verified in a double-blind study.

What Is a Double-Blind Study? 

A double-blind study is an experiment where both researchers and participants are “blind to” the crucial aspects of the study, such as the hypotheses, expectations, or the allocation of subjects to groups. In double-blind clinical trials, neither the experimenters nor the participants are aware of who is receiving a treatment.

Why Do a Double-Blind Study?

The main purpose of double-blind studies is to minimize the effects of experimenter bias . In other words, the results of the research are less likely to be affected by external factors, such as the experimenters verbally or nonverbally communicating their assumptions about the treatment’s efficiency or the expectations of the participants.

Double-blind studies serve as an invaluable scientific method in the pharmaceutical industry trials where they are regularly used for determining the impact of new medications. Double-blind studies are the very foundation of modern evidence-based medicine. They are often referred to as the gold standard for testing medications, that is, the most accurate test available. 

While they are best known for their application in medicine, double-blinded studies are widely used to validate theories and ideas in many other fields including agriculture, biology, chemistry, engineering, forensics, and social sciences. 

Example of Double-Blind Study

Identifying successful treatments is a complex procedure. Let’s say that a physician prescribes a new medication to a patient. After taking the medication, the patient reports improvement in his or her condition. Yet this doesn’t simply mean that the treatment is effective. In fact, in many cases patients will see improvements even when they are not taking active medication. 

In order to properly test the medication, a double-blind study will have to take place in which the experimenter (acting as the physician) administers either the medication or a placebo to the participant (acting as the patient). Only a third-party knows whether the medication was real or not. The participant's answers about their treatment will be recorded and sent to that third party.

Double-blind studies aren't just used to test new medication. A double-blind study was used to see if airport security dogs could sniff out COVID!

Double-Blind Studies and Placebo Effect

The placebo effect is a crucial component of double-blind studies. 

A placebo is an inactive substance that has no effect on the individual who is taking it. It looks just like the medication that is being tested so that the participants can’t say whether they are receiving the treatment or not. 

How to Conduct a Double-Blind Study

Subjects in double-blind studies are typically divided into three different groups: treatment or experimental group, placebo group, and control group. 

Participants who are not receiving any treatment are placed in the control group. This group serves as a baseline for determining whether the medication in question has any significant effects. If the control group gets better over time, then this improvement will set a standard against which the other two groups are compared. 

People placed in the treatment group are given the actual medication, while subjects in the placebo group are offered a placebo pill. Neither the participants in the treatment and placebo groups nor the experimenters have the information on who is receiving the real drug.

At the end of the trial, data collected from the groups are compared to determine if the treatment had the expected outcome. If subjects in the placebo group fare better than the control group, this positive development can be attributed to the participants’ belief that the pill works. But if people in the treatment group improve more than those in the placebo one, then the results can be attributed to the effect of the medication.

Other Types of Blind Studies

Several different types of blind studies are being used in research, such as double-blind comparative studies, single-blind studies, and triple-blind studies.

Double-blind comparative studies

In double-blind comparative studies, one group of participants is given a standard drug instead of a placebo. These studies compare the effects of new medicine and an old one whose impact has already been proven. This kind of study is useful in determining whether a new treatment is more effective than the existing one. 

Single-blind studies

In single-blind studies, only the participants are not informed whether they are receiving the real treatment. The experimenters, on the other hand, know which participants belong to which group.  

Triple-blind studies

Triple-blind studies are clinical trials in which knowledge about the treatment is hidden not only from subjects and experimenters but also from anyone involved in organizing the study and data analysis. 

Limitations of Double-Blind Studies

Despite their significance, double-blind studies hold a number of limitations and are not applicable to every type of research.

Number of Participants

To be effective, a double-blind study must include at least 100 participants and preferably as many as 300. Although effective treatments can also be proven in some small-scale trials, many double-blind studies are too limited in size to provide a representative sample and establish meaningful patterns. Studies involving fewer than 30 participants generally can't provide proof of a theory. 

Types of Double-Blind Studies 

Double blinding is not feasible in all types of trials. For instance, it is not possible to design studies on therapies such as acupuncture, physical therapy, diet, or surgery in a double-blind manner. In these cases, researchers and participants can’t be kept unaware of who is receiving therapy .

Nocebo Effect

Participants in clinical trials must be informed of the possible side effects that may result from the experimental treatment. However, the mere suggestion of a negative outcome may lead to the adverse placebo effect, also known as the nocebo effect. It can result in participant dropouts and the need for additional medications to treat the side effects.

In research, the use of a placebo is acceptable only in situations when there is no proven acceptable treatment for the condition in question. For ethical reasons, participants must always be informed of the possibility that they will be given a placebo. As a consequence, some participants may think that they feel the effects of the placebo, which makes them believe that they are in the treatment group. This high positive expectancy is a disadvantage that can lead to a misinterpretation of the results.

Costs of Double-Blind Studies

Double-blind procedures are very expensive. They may take several months to complete, as experiments often require numerous trials using different groups in order to collect enough data. As a result, double-blind studies can cost up to several million dollars, depending on the amount of work required and the industry in which the product is being tested.

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What Is a Double Blind Experiment?

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In many experiments, there are two groups: a control group and an experimental group . The members of the experimental group receive the particular treatment being studied, and the members of the control group do not receive the treatment. Members of these two groups are then compared to determine what effects can be observed from the experimental treatment. Even if you do observe some difference in the experimental group, one question you may have is, “How do we know that what we observed is due to the treatment?”

When you ask this question, you are really considering the possibility of lurking variables . These variables influence the response variable but do so in a way that is difficult to detect. Experiments involving human subjects are especially prone to lurking variables. Careful experimental design will limit the effects of lurking variables. One particularly important topic in the design of experiments is called a double-blind experiment.

Humans are marvelously complicated, which makes them difficult to work with as subjects for an experiment. For instance, when you give a subject an experimental medication and they exhibit signs of improvement, what is the reason? It could be the medicine, but there could also be some psychological effects. When someone thinks they are being given something that will make them better, sometimes they will get better. This is known as the placebo effect .

To mitigate any psychological effects of the subjects, sometimes a placebo is given to the control group. A placebo is designed to be as close to the means of administration of the experimental treatment as possible. But the placebo is not the treatment. For example, in the testing of a new pharmaceutical product, a placebo could be a capsule that contains a substance that has no medicinal value. By use of such a placebo, subjects in the experiment would not know whether they were given medication or not. Everyone, in either group, would be as likely to have psychological effects of receiving something that they thought was medicine.

Double Blind

While the use of a placebo is important, it only addresses some of the potential lurking variables. Another source of lurking variables comes from the person who administers the treatment. The knowledge of whether a capsule is an experimental drug or actually a placebo can affect a person’s behavior. Even the best doctor or nurse may behave differently toward an individual in a control group versus someone in an experimental group. One way to guard against this possibility is to make sure that the person administering the treatment does not know whether it is the experimental treatment or the placebo.

An experiment of this type is said to be double blind. It is called this because two parties are kept in the dark about the experiment. Both the subject and the person administering the treatment do not know whether the subject in the experimental or control group. This double layer will minimize the effects of some lurking variables.

Clarifications

It is important to point out a few things. Subjects are randomly assigned to the treatment or control group, have no knowledge of what group they are in and the people administering the treatments have no knowledge of which group their subjects are in. Despite this, there must be some way of knowing which subject is in which group. Many times this is achieved by having one member of a research team organize the experiment and know who is in which group. This person will not interact directly with the subjects, so will not influence their behavior.

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double-blind experiment

An experiment designed such that neither the participants nor the researchers observing them know which participants are in the experimental and control groups until after the observations are complete. Double-blind experiments are particularly important in the field of medicine because they control for both the placebo effect and unconscious bias on the part of the researchers — two factors that can make the results of a medical study difficult to interpret. To learn more, visit our side trip Fair tests in the field of medicine .

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Blinding in Clinical Trials: Seeing the Big Picture

Thomas f. monaghan.

1 Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA

Christina W. Agudelo

2 Division of Cardiovascular Medicine, Department of Medicine, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; [email protected] (C.W.A.); [email protected] (J.M.L.)

Syed N. Rahman

3 Department of Urology, Yale University School of Medicine, New Haven, CT 06520, USA; [email protected]

Alan J. Wein

4 Division of Urology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA; [email protected]

Jason M. Lazar

Karel everaert.

5 Department of Human Structure and Repair, Ghent University, 9000 Ghent, Belgium; [email protected]

Roger R. Dmochowski

6 Department of Urological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA; [email protected]

Associated Data

Not applicable.

Blinding mitigates several sources of bias which, if left unchecked, can quantitively affect study outcomes. Blinding remains under-utilized, particularly in non-pharmaceutical clinical trials, but is often highly feasible through simple measures. Although blinding is generally viewed as an effective method by which to eliminate bias, blinding does also pose some inherent limitations, and it behooves clinicians and researchers to be aware of such caveats. This article will review general principles for blinding in clinical trials, including examples of useful blinding techniques for both pharmaceutical and non-pharmaceutical trials, while also highlighting the limitations and potential consequences of blinding. Appropriate reporting on blinding in trial protocols and manuscripts, as well as future directions for blinding research, will also be discussed.

1. Introduction

Randomized clinical trials are a gold standard in evidence-based medicine because findings from these studies reflect the highest possible level of evidence which may be garnered from an original research study [ 1 ]. Randomized clinical trials tend to be highly tailored to a specific research question but, for a vast majority of interventions and outcomes, blinding is widely viewed as a core tenet of sound clinical trial study design [ 2 , 3 , 4 ].

Despite exponential growth in the number of clinical trials conducted yearly over the past two decades [ 5 ], multiple authors contend that the methodological quality of clinical trials has remained stagnant or even declined [ 6 , 7 ], such that true practice-guiding evidence on a broad range of medial topics paradoxically lags behind [ 8 , 9 , 10 ]. Blinding is one aspect of clinical trial design that remains particularly underutilized—although this methodological feature is not universally attainable, blinding is still implemented in only a fraction of clinical trials in which it is, in fact, deemed feasible [ 11 , 12 , 13 ]. Accordingly, it stands to reason that greater emphasis on addressing pervasive misconceptions about blinding in medical research is key to reconciling the growing divide between current research trends and actual practice needs [ 14 ]. Furthermore, because blinding is relevant to data analysis in the broadest sense, a sound understanding of blinding should be considered a prerequisite for evidence-based best practice, and thus of equal importance to providers and patients alike [ 15 , 16 ].

This article will review general principles for blinding in clinical trials, including examples of useful blinding techniques for both pharmaceutical and non-pharmaceutical trials, while also highlighting the limitations and potential consequences of blinding. Appropriate reporting on blinding in trial protocols and manuscripts, as well as future directions for blinding research, will also be discussed. Note that this article will focus on blinding in clinical trials, where it is most often discussed, but the relevance of blinding spans the gamut of study designs, from late-stage randomized interventional trials to retrospective observational studies (e.g., blinded outcome assessors) [ 17 , 18 ].

2. What Is Blinding?

In an unblinded, or “open”, study, information about the assigned interventions is available to all people and groups involved in the research. Blinding, or “masking”, is the process by which information that has the potential to influence study results is withheld from one or more parties involved in a research study.

Importantly, the topic of blinding must be distinguished from allocation concealment. Allocation concealment is the process by which investigators and participants enrolled in a clinical study are kept unaware of upcoming group assignments until the moment of assignment [ 19 ]. Allocation concealment is a core tenet of proper study randomization and plays a key role in preventing selection bias [ 20 ]. Blinding, in contrast, refers to the act of withholding information about the assigned interventions from people involved in the trial from the time of group assignment until the experiment is complete. While proper randomization minimizes the differences between treatment groups at the beginning of a trial, it does not prevent differential treatment of study groups during the trial, nor does it prevent differential interpretation and analysis of study outcomes [ 21 ].

3. Why Do We Blind?

We blind because the potential for bias is everywhere. Bias can take numerous shapes and forms when people involved in a research study are privy to information about the assigned interventions [ 22 ]. Participant knowledge of their group allocation can bias expectations, adherence to the trial protocol, treatment-seeking behavior outside the trial, and assessment of the effectiveness of an intervention [ 23 ]. Differential treatment, attention, or attitudes toward subjects by a non-blinded healthcare team or other members of the research staff also pose a major threat to unbiased outcomes [ 24 , 25 ]. Importantly, once bias is introduced from any one of these potential sources, there exist no analytical techniques by which to reliably correct for this limitation [ 21 ].

Several lines of empirical evidence demonstrate the direct effects of non-blinding on clinical trial outcomes. One systematic review from Hróbjartsson et al. concluded that attrition is significantly more frequent among controls versus subjects assigned to the experimental group when participants are not blinded—a phenomenon not common to well-designed participant-blinded trials [ 26 , 27 ]. Moreover, participant-reported outcomes were found to be exaggerated by 0.56 standard deviations overall in trials of non-blinded versus blinded participants, with an even greater discrepancy in trials investigating invasive procedures [ 26 ]. In three separate meta-analyses from Hróbjartsson et al. on observer bias in randomized clinical trials, non-blinded versus blinded outcome assessors were found to generate exaggerated hazard ratios by an average of 27% in studies with time-to-event outcomes [ 28 ], exaggerated odds ratios by an average of 36% in studies with binary outcomes [ 29 ], and a 68% exaggerated pooled effect size in studies with measurement scale outcomes [ 30 ]. Taken together, the four meta-analyses from Hróbjartsson et al. indicate that participant blinding and assessor blinding similarly lend to exaggerated effect sizes, although the three analyses on observer bias collectively suggest that the type of variable assessed influences how large of an effect blinding may have on study results.

The relevance of blinding in mitigating bias is perhaps most easily appreciated in studies involving subjective outcomes. However, many seemingly objective outcomes rely on interpretation of participant data and thus are also characterized by subjective elements (e.g., electrocardiogram scan interpretation for myocardial infarction) [ 31 ]. Further, even unequivocally objective outcomes, such as time to death, can be indirectly affected by factors such as the use of advance directives, concurrent interventions, and follow-up intensity [ 31 ]. Correspondingly, while some meta-analyses have reported more robust evidence of bias with subjective versus objective outcomes [ 32 ], this finding is inconsistent, and multiple other studies have reported no appreciable difference in estimated treatment effect based on the degree of outcome subjectivity [ 29 , 33 ]. Thus, for both subjective and objective outcomes, current evidence suggests that blinding can play a potentially major role in mitigating threats to internal and construct validity [ 34 ].

4. Who and What Do We Blind?

Current literature has identified as many as 11 distinct groups meriting unique consideration when it comes to blinding: (1) participants, (2) care providers, (3) data collectors and data managers, (4) trial managers, (5) pharmacists, (6) laboratory technicians, (7) outcome assessors (study personnel who collect outcome data), (8) outcome adjudicators (personnel who confirm that outcomes meet prespecified criteria), (9) statisticians, (10) members of safety and data monitoring committees, and (11) manuscript writers [ 35 ].

In a blinded clinical study, treatment assignment is the information most frequently withheld from these groups [ 35 ]. However, in many cases, blinding of some of the aforementioned groups to additional information is also feasible. For example, laboratory technicians, outcome assessors, and outcome adjudicators may also be blinded to basic demographic and clinical characteristics of the study population, as well as the overall purpose of the trial [ 35 ].

Consistent with the significant heterogeneity as to “who” and “what” may be blinded, it is important to appreciate blinding on a graded continuum rather than as an all-or-nothing phenomenon, wherein the blinding of some study groups to pertinent information as feasible (i.e., “partial blinding”) can tangibly improve the strength of trial results—even when maximal blinding of all study groups cannot be achieved [ 35 , 36 ].

5. How Do We Blind?

A multitude of techniques have been described for blinding all people and groups involved in clinical trials. The researcher’s specific approach to blinding will ultimately be highly dependent on the specific parties being blinded as well as the research question and intervention at hand. In fact, there exists considerable flexibility in blinding—even beyond the strategies for blinding subsequently highlighted in this section, investigators may feasibly create their own novel blinding technique, so long as (1) the technique successfully conceals pertinent information about the groups and (2) does not impair the ability to accurately assess or adjudicate outcomes [ 37 ].

Boutron et al. systematically reviewed blinding in randomized control trials assessing pharmacologic treatments, organizing their results to provide an excellent inventory of practical methods to (1) establish blinding of participants and providers, (2) maintain blinding (i.e., prevent unblinding), and (3) blind outcome assessors [ 38 ]. Common methods to establish participant/provider blinding include centralized preparation of similar capsules or tablets, bottles, and syringes; flavoring to mask the specific taste of active oral treatments; and double-dummy procedures. (A double-dummy technique is the use of more than one placebo for the maintenance of blinding, particularly in cases when two treatments under investigation cannot be made identical, wherein subjects are assigned to different sets of treatment and more than one group may receive placebo. For example, in a trial designed to compare an oral tablet medication with a medication administered by intramuscular injection, an indistinguishable placebo can be prepared for both the tablet and injection, and one group may receive the active medication tablet and placebo injection, with another group receiving the placebo tablet and active medication injection.) Strategies for reducing the risk of unblinding include centralized dosage adaptation as warranted, centralized evaluation for side effects, partial information about side effects, and use of an “active placebo” (sugar pill which mimics expected side effects of the active treatment). Methods for blinding outcome assessors typically rely on centralized assessment of complementary investigations, clinical examinations, and adjudication of clinical events.

Blinding in non-pharmaceutical trials is undoubtedly faced with several unique challenges related to the complexity and physical component of such interventions, participant and physician acceptance, and broader ethical and safety considerations [ 39 , 40 ]. Accordingly, relative to pharmaceutical trials, blinding is typically implemented even less frequently in those investigating surgical procedures, medical devices, and participative interventions (e.g., rehabilitation) [ 41 ]. Nevertheless, compared to pharmaceutical trials, blinding in these trials is of no less relevance in the pursuit of true practice-shaping evidence [ 41 , 42 ]. In fact, blinded interventional trials are often practical, and may even feasibly involve a placebo group (i.e., “sham procedure”) [ 13 ]. Figure 1 provides examples of sham procedures for surgical interventions and other non-pharmacological clinical trials, as published in a separate systematic review from Boutron and colleagues [ 43 ].

Sham procedure performed according to the category of treatment assessed. Reprinted with permission from ref. [ 43 ]. Copyright 2007 Boutron et al. Full text available from https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0040061#s5 .

In trials comparing two similar invasive procedures, particularly those performed under general anesthesia or heavy sedation, blinding of participants can be relatively straightforward [ 21 ]. Notably, however, blinding of participants may even be feasible when surgical interventions differ significantly. Namely, there exist several well-described recent examples of investigators devising highly creative solutions to maintain participant blinding in invasive interventional trials, including imitation of the surgical access point, replication of visual, auditory, and physical cues in the operating room, matching the duration of experimental and control procedures, and standardization of additional care (e.g., diagnostic scans, perioperative medical management, etc.) [ 13 ].

Beyond these measures, a handful of studies have even managed to blind surgeons to the intervention being performed. For example, in one randomized control trial of electrothermal therapy for chronic lower back pain, surgeons inserted an intradiscal catheter under fluoroscopic guidance in all cases, at which point an independent technician connected the catheter to a generator and delivered either electrothermal energy (experimental group) or did not (control group) [ 44 ]. A trial on palatal implants for obstructive sleep apnea blinded proceduralists through the use of a manufacturer-preloaded delivery system containing either an implant (active treatment) or no implant (sham) [ 45 ]. While blinding of surgeons is seldom practical, eliminating their role in post-operative care, follow-up, and additional treatment is often feasible for minimizing this potential source of bias [ 46 ].

Even in the absence of surgeon blinding, it is often possible to blind other members of the care team and study staff from information that has the potential to bias study results. Simple measures such as uniform dressings large enough to cover all potential incision sites have been used to successfully blind other members of the post-operative care team [ 21 ]. Blinding of outcome assessors, while uncommonly performed in surgical trials, is frequently practical through simple techniques such as the use of independent assessors, concealed incisions, and blinding of digital images [ 12 ]. Figure 2 depicts methods for blinding key groups in randomized control trials for different non-pharmacological clinical trials from Boutron et al. [ 43 ].

Methods of blinding participants, health care providers, or other caregivers that rely on the category of treatment and comparator assessed. Reprinted with permission from ref. [ 43 ]. Copyright 2007 Boutron et al. Full text available from https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0040061#s5 .

6. Limitations of Blinding

Although blinding is generally viewed as a very effective method by which to eliminate bias, blinding does pose some inherent limitations, and it behooves clinicians and researchers to be aware of such caveats. Blinding often requires considerable effort and expense [ 47 ]. Blinding also has a well-established negative impact on study recruitment [ 48 , 49 , 50 ]. Additionally, blinding inherently deviates from real-world experience, making it a hallmark feature of trials which aim to maximize the likelihood of establishing the efficacy of an intervention by testing it in an ideal setting (i.e., “explanatory trials”), but potentially less relevant for trials which aim to generate situations that are as close to routine practice as possible (i.e., “pragmatic trials”) [ 51 ]. Blinding has also been suggested to potentially adversely impact subsequent care after the conclusion of a clinical trial [ 52 ].

In contrast to open-label studies, blinded clinical trials are inherently susceptible to the phenomenon of unblinding (i.e., “code-breaking”). The term “unblinding” is often used to describe the formal process by which subjects and/or investigators are made aware of a participant’s treatment assignment according to prespecified contingencies (e.g., in the case of a medical emergency which compromises a participant’s safety). However, even in the absence of formal unblinding, subjects in either the intervention or control group may feasibly come to suspect their assignment status using more subtle clues, such as the presence (or absence) of signature medication effects or side effects [ 53 , 54 ]. In fact, potential threats to blinding are pervasive and multifaceted—there have been documented cases of researchers intentionally subverting blinding by comparing pills or viewing restricted notes and, more recently, instances of trial participants connecting through social media and collaborating to deduce their treatment allocation [ 55 , 56 , 57 ].

Unblinding has several deleterious effects that can threaten the validity of trial results [ 58 , 59 ]. Subjects in a placebo arm who discover or suspect that they are not receiving active treatment may become upset or uncooperative (i.e., “resentful demoralization”), access interventions outside of the trial (i.e., “compensatory rivalry”, which inherently increases the risk of “contamination” (i.e., when members of the control group are inadvertently exposed to the intervention)), exaggerate negative responses (i.e., “biased event reporting”), or even withdraw from the trial [ 60 , 61 ]. Similarly, empathetic caregivers who know subjects to be controls may provide them with non-study, but effective, interventions (i.e., “cointerventions”) [ 62 ]. Conversely, participants who suspect or know that they are on the “better” treatment may downplay mild side-effects, while clinicians with this information may downplay participants’ symptoms and underreport “soft” clinical findings (also “biased event reporting”, but typically in the opposite direction compared to controls) [ 62 ].

Multiple recent analyses of blinded trials published in high-impact medical journals have concluded that the implementation of blinding is inconsistent and successful in perhaps fewer than 50 percent of cases [ 62 ]. Various efforts have been made to quantitively assess blinding, which most commonly utilize a blinding questionnaire or survey, and ask subjects in both the experimental and control groups to guess their treatment allocation [ 63 ]. Several methods have been employed in analyzing these data, including chi-square and McNemar’s tests, a standard Kappa statistic, and multiple blinding indices [ 63 , 64 , 65 ]. However, it should be noted that an assessment of blinding success is only seldom performed [ 66 , 67 , 68 ], and has even been criticized for the inherent limitations of this process [ 69 ]—many of which centering on the fact that end-of-trial tests for “blindness” cannot be reliably distinguished from hunches about efficacy [ 62 ]. In other words, participant responses to end-of-trial blinding surveys are likely influenced by prior assumptions and expectations regarding treatment efficacy, such that beliefs about allocation may still cause bias even when blinding succeeds in making these beliefs independent of actual allocation [ 70 ]. Citing these reasons, the most recent “Current Consolidated Standards of Reporting Trials” (CONSORT) statement no longer advocates for testing of blinding success, reflecting a major divergence from prior renditions of CONSORT guidelines [ 71 ]. The topic of evaluating and reporting blinding success remains debated and is highly complex, but there does exist a relative consensus regarding the need for a greater understanding of the bias-generating consequences that result from its loss, irrespective of whether they arise from the loss of blindness, per se, or rather from beliefs about allocation or another cause [ 62 ].

The limitations of blinding with respect to recruitment, applicability to routine practice, and analysis have led some authors to challenge the role of participant and clinician blinding as a universal gold standard in evidence acquisition. Anand et al. emphasize that blinding of participants and clinicians requires careful consideration of the negative effects of blinding against its potential benefits, as guided by the following key questions: (1) whether blinding is needed for a scientifically sound result; (2) whether changes in participant or clinician awareness of assignment status will cause a change in behavior that influences results; (3) whether there is a risk of excessive harm with blinding and, if so, whether said risk is justified by the importance of the study findings; and (4) whether the financial cost of blinding compromises spending on other aspects of trial integrity [ 53 ]. Note that recent criticisms of blinding from Anand et al. and others primarily center on the topic of participant and/or clinician blinding—there remains a relative consensus regarding the critical importance of objective outcomes, blinded outcomes assessment, and blinded adjudication of outcomes in mitigating major sources of bias in clinical trials [ 53 ].

7. Blinding: Reporting Responsibly

The terms single-blind, double-blind, and triple-blind are often used to describe studies in which one, two, or three parties, respectively, are blinded to information about the treatment groups. Recall, however, that up to 11 discrete groups merit unique consideration with respect to blinding in clinical trials [ 35 ]. Correspondingly, there has long existed great variability in textbook definitions and clinician interpretations of these terms [ 72 ], which is particularly problematic given that study authors often fail to specify who, exactly, has been blinded [ 73 ]. For example, a sample of randomized clinical trials published in 2001 found that more than half of “double-blind” studies failed to describe the blinding status of any person involved in the trial [ 74 ]. Moreover, on a follow-up survey sent to trial authors, 15 different operational meanings of the term “double-blind” were reported by the investigators, who typically believed that their preferred definition was the most widely used [ 74 ].

In view of the high potential for misinterpretation, authors of the most recent CONSORT (2010) statement instruct researchers to “abandon [the] use” of “double-blind” and related terms [ 71 ]. Instead, the 2010 CONSORT guidelines direct authors to “explicitly report blinding status”, including who is and is not blinded, what information is concealed, and how blinding is performed [ 75 ]. Further, if relevant, authors must provide a description of the similarity of the interventions and procedures used for blinding [ 75 ]. (Specification of how blinding was performed, as well as a description of an intervention’s similarity, were both “noteworthy specific changes” from early renditions of the CONSORT statement [ 75 ], motivated by the need for greater “evidence of the method of blinding” [ 71 ].) The “Standard Protocol Items: Recommendations for Interventional Trials” (SPIRIT) 2013 statement similarly directs authors to specify who will be blinded and how blinding will be accomplished in clinical trial protocols [ 76 ].

Despite these increasingly explicit consensus recommendations, there still exist major discrepancies in how blinding is reported in registered protocols and publications, as evidenced by continued widespread suboptimal adherence to current CONSORT and SPIRIT guidelines [ 77 , 78 , 79 ]. Several strategies have been proposed for improving the quality of reporting on blinding in clinical trials. One practical option recently proposed by Lang et al. is to detail blinding status using a standardized “Who Knew” table [ 35 ]. Although such a practice has not yet gained widespread traction, the author’s table aptly illustrates the extent to which blinding should be described to ensure transparency in research methodology ( Table 1 ).

A standard table for reporting the use of blinding in randomized trials of pharmaceutical interventions.

( a ) Other groups or individuals in a trial that were capable of being blinded should be listed in the table, and whether or not they were blinded in the study should be indicated. Individuals with dual responsibilities, such as caregiving and data collecting, should be identified by combining the entries in the same row heading. ( b ) Although group assignment is the information most commonly withheld in a blinded trial, data assessors, such as pathologists and radiologists, are often blinded to the purpose of the trial, group assignment, and the demographic and clinical characteristics of participants whose biopsy samples or images they are interpreting. ( c ) In many cases, authors should determine before the trial begins whether the method of blinding had a reasonable chance of being effective, including establishing the similarity between active and placebo preparations and the bioequivalent availability for two or more active drugs [ 80 ]. Testing the effectiveness of blinding after the trial has ended is uninformative because the results cannot be separated from pre-trial expectations of the success of the intervention [ 47 ]. ( d ) If blinding has been compromised, authors should report the fact and indicate the potential implications the loss of blinding might have for interpreting the results [ 80 ]. Reprinted with permission from ref. [ 35 ]. Copyright 2020 Lang et al. Full text available from https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-020-04607-5 .

8. Future Directions

Numerous studies have used and not used blinding. Comparatively, however, far fewer papers have attempted to comprehensively review blinding in clinical trials, and several questions remain unanswered. The magnitude of the estimated treatment effect associated with participant blinding status has been shown to vary considerably across different studies [ 29 ]. As detailed previously, the three separate meta-analyses from Hróbjartsson et al. on observer bias collectively suggest that the type of variable also influences the magnitude of the effect which blinding may exert on study results [ 28 , 29 , 30 ]. Further, a subset of studies have found non-blinded assessors to significantly favor control, rather than experimental, interventions, corresponding to a comparable degree of observer bias in the opposite direction, but the reason for this remains unclear [ 30 ]. Moreover, compared to participants and outcome assessors, the impact of blinding of other trial personnel and healthcare professionals on estimated treatment effect is even less well-established [ 32 , 33 ]. Therefore, multiple factors appear to impact the magnitude of bias imposed by a lack of blinding, and recent meta-epidemiological evidence suggests that many relevant study factors remain incompletely characterized in this regard [ 33 ]. The effects of unblinding all above-mentioned study groups on study outcomes likewise remain poorly characterized.

There exist several additional facets of clinical trial study design which also merit greater investigation in relation to blinding status. Historically, placebos constituted the primary comparator arm in most pharmacologic randomized control trials, but trials involving active best-of-care comparator arms and other non-placebo background therapies have grown in popularity in recent years [ 81 , 82 ]. Surgical trials are seemingly even more heterogeneous in this regard, as new surgical interventions may be tested against placebo (i.e., “sham procedure”), but also against a similar surgical/invasive intervention, dissimilar surgical/invasive intervention, pharmacotherapy, participative intervention (e.g., physical therapy), or active surveillance/watchful waiting [ 41 ]. Accordingly, whether specific characteristics of a study’s comparator arm(s) modify the effects of blinding or consequences of unblinding merits further study [ 83 ]. Additionally, although blinding is infrequently incorporated into early-stage clinical trials [ 84 ], we are unaware of studies assessing the effects of blinding as a function of study phase, and it may be revealing to assess the relative effect of blinding in phase 2 versus phase 3 trials—particularly in cases where phase 2 and phase 3 trials show divergent results [ 85 ]. We also advocate for a more simplified and standardized approach to incorporating blinding in power analyses and sample size re-estimation for adaptive trials [ 86 , 87 , 88 ].

Author Contributions

Conceptualization, T.F.M. and R.R.D.; writing—original draft preparation, T.F.M. and R.R.D.; writing—review and editing, T.F.M., C.W.A., S.N.R., A.J.W., J.M.L., K.E., and R.R.D. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

Thomas F. Monaghan has no direct or indirect commercial incentive associated with publishing this article and certifies that all conflicts of interest relevant to the subject matter discussed in the manuscript are the following: Alan J. Wein has served as a consultant for Medtronic, Urovant, Antares, and Viveve, outside the submitted work. Karel Everaert is a consultant and lecturer for Medtronic and Ferring and reports institutional grants from Allergan, Ferring, Astellas, and Medtronic, outside the submitted work. The additional authors have nothing to disclose.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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• 2.1 Independent Variable
• 2.2 Dependent Variable
• 2.3 Controlled Variables
• 2.4 Third Variable
• 3.1 Control Group
• 3.2 Research Bias
• 3.3.1 Placebo Effect
• 3.3.2 Double Blind Method
• 4.1 Randomized Controlled Trials
• 4.2 Pretest-Posttest
• 4.3 Solomon Four Group
• 4.4 Between Subjects
• 4.5 Within Subject
• 4.6 Repeated Measures
• 4.7 Counterbalanced Measures
• 4.8 Matched Subjects

It is very easy for a researcher, even subconsciously, to influence experimental observations, especially in behavioral science, so this method provides an extra check.

For example, imagine that a company is asking consumers for opinions about its products, using a survey .

There is a distinct danger that the interviewer may subconsciously emphasize the company's products when asking the questions. This is the major reason why market research companies generally prefer to use computers, and double blind experiments, for gathering important data.

The Blind Experiment

The blind experiment is the minimum standard for any test involving subjects and opinions, and failure to adhere to this principle may result in experimental flaws.

The idea is that the groups studied, including the control , should not be aware of the group in which they are placed. In medicine, when researchers are testing a new medicine, they ensure that the placebo looks, and tastes, the same as the actual medicine.

There is strong evidence of a placebo effect with medicine, where, if people believe that they are receiving a medicine, they show some signs of improvement in health. A blind experiment reduces the risk of bias from this effect, giving an honest baseline for the research, and allowing a realistic statistical comparison.

Ideally, the subjects would not be told that a placebo was being used at all, but this is regarded as unethical.

The Double Blind Experiment

The double blind experiment takes this precaution against bias one step further, by ensuring that the researcher does not know in which group a patient falls.

Whilst the vast majority of researchers are professionals, there is always a chance that the researcher might subconsciously tip off a patient about the pill they were receiving. They may even favor giving the pill to patients that they thought had the best chance of recovery, skewing the results.

Whilst nobody likes to think of scientists as dishonest, there is often pressure, from billion dollar drug companies and the fight for research grants, to generate positive results.

This always gives a chance that a scientist might manipulate results, and try to show the research in a better light. Proving that the researcher carried out a double blind experiment reduces the chance of criticism.

Other Applications

Whilst better known in medicine, double blind experiments are often used in other fields. Surveys , questionnaires and market research all use this technique to retain credibility.

If you wish to compare two different brands of washing powder, the samples should be in the same packaging. A consumer might have an inbuilt brand identity awareness, and preference, which will lead to favoritism and bias.

An example of the weakness of single blind techniques is in police line-ups, where a witness picks out a suspect from a group. Many legal experts are advocating that these line-ups should be unsupervised, and unprompted.

If the police are fixed on bringing a particular subject to justice, they may consciously, or subconsciously, tip off the witness. Humans are very good at understanding body language and unconscious cues, so the chance of observer's bias should be minimized.

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Double-Blinded Study

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• Ashwini Padhi 2 &
• Naomi Fineberg 3  

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Double-blind study ; Double-masked studies

Blinded studies are part of a scientific method to prevent research outcomes from being influenced by various biases such as patient expectations (placebo effect) and experimenter expectancy (observer bias). The term blind is a figurative extension of the literal idea of blindfolding someone. The opposite of a blinded trial is an open trial. An open trial or open-label trial is a clinical trial in which both the researchers and participants know which treatment is being administered. There can be varying degrees of blinding such as single-blind, double-blind, triple-blind, etc. Double-blinded study is a term used to described a study in which both the investigator or the participant are blind to (unaware of) the nature of the treatment the participant is receiving. In a double-blind experiment, neither the individuals nor the researchers know who belongs to the control group and the experimental group. Double-blind trials...

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Department of Psychiatry, Queen Elizabeth II Hospital, AL7 4HQ, Hertfordshire Partnership NHS Foundation Trust, Welwyn Garden City, Hertfordshire, UK

Ashwini Padhi

Hertfordshire Partnership NHS Foundation, Community Mental Health Team, Edinburgh House, 82-90 London Road, AL1 1NG, St. Albans, Hertfordshire, UK

Naomi Fineberg

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Ian P. Stolerman

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Padhi, A., Fineberg, N. (2010). Double-Blinded Study. In: Stolerman, I.P. (eds) Encyclopedia of Psychopharmacology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68706-1_1425

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What is a Double-Blind Trial?

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When drugs or vaccines are being trialed for their effectiveness, there are typically several stages. Double-blind trials are seen as the most reliable type of study because they involve neither the participant nor the doctor knowing who has received what treatment. The aim of this is to minimize the placebo effect and minimize bias.

How they work

In double-blind trials, the treatment patients have is unknown to both patients and doctors until after the study is concluded. This differs from other types of trials, such as simple blind trials where only the patients are unaware of the treatment they are receiving, whereas the doctors know.

Double-blind trials are a form of randomized trials and can be ‘upgraded’ to triple-blind trials, in which the statisticians or data clean-up personnel are also blind to treatments.

To be effective, it is generally recommended that double-blind trials include around 100-300 people. If treatments are highly effective, smaller numbers can be used but if only 30 or so patients are enrolled the study is unlikely to be beneficial.

The assignment of patients into treatments is typically done by computers, where the computer assigns each patient a code number and treatment group. The doctor and patients only know the code number to avoid bias, hence allowing the study to be double-blind.

Double-blind trials can come in different varieties. Double-blind, placebo-controlled studies involve no one knowing the treatment assignments to remove the chance of placebo effects. In a double-blind comparative trial, a new treatment is often compared to the standard drug. This allows researchers to compare an established drug to a new one to establish which one is more advantageous.

However, unlike double-blind, placebo-controlled trials, they are not very good at statistically evaluating if a treatment is effective overall.

Benefits of double-blind trials

Double-blind trials remove any power of suggestion, as no one involved knows the treatment patients receive. This means that doctors carrying out the study do not know and cannot accidentally tip off participants. Similarly, the doctors not being aware of the treatments means they do not unconsciously bias their interpretation of the study results.

The main principle behind double-blind and randomized trials, as opposed to simple blind trials, is to avoid bias in the treatment or experimental set-up. For example, if researchers are aware of the different treatment groups are getting, they may avoid assigning more unwell patients to the treatment group. Therefore, any effect seen by the treatment may have been related to how unwell a patient was to start with, rather than the efficacy of the drug.

COVID-19 and double-blind trials

Double-blind trials are usually needed for drugs and treatments to get approval to be used in many countries. However, good, comprehensive double-blind trials take time and require many participants. This has been especially problematic during the COVID-19 pandemic, as the world has searched for pharmaceutical treatment options to improve survival and for vaccines to prevent the spread of this virus.

In terms of treatment, many drugs have been tested in double-blind trials. The antiviral nucleoside analog remdesivir has been tested in several double-blind trials and was the first drug to gain full FDA approval for use against COVID-19 in October 2020.

However, the results of trials have been conflicting, and some experts remained unconvinced of its benefits. In November 2020, the World Health Organization recommended against the use of the drug for COVID-19 and a global randomized trial came to the conclusion in February 2021 that remdesivir has little to no effect when used on hospitalized COVID-19 patients. The drug is still used in the US.

Multiple candidates for a COVID-19 vaccine have been identified and moved on to phase II and phase III trials, which often involve double-blind methods. These need to be conducted over meaningful timeframes to ensure any initial differences between the control and the treatment groups last in the long term.

Several different vaccines are now available (March 2021) due to mixed approval and emergency approval by governments and organizations. This has been an exceptional time for vaccine trials as the typical course of development has been sped up. What would usually take years has taken months.

Many countries have given limited or early approval to vaccines for emergency use before detailed phase III data has been publicized, based on preliminary evidence of effectivity and safety. This comes with some risks.

Another topic of discussion that has come about as a result of COVID-19 is the ethics of keeping patients blind during the trial as vaccine effectivity is supported. Whilst keeping the blind aspect is essential to achieving valuable and reliable information about long-term effects, there is an argument that blind participants who have received a placebo should be able to receive a vaccine as more become available.

• Cancer Research UK. 2019. Randomized Trials . [online] Available at: <https://www.cancerresearchuk.org/find-a-clinical-trial/what-clinical-trials-are/randomised-trials> [Accessed 25 July 2020].
• European Centre for Disease Prevention and Control. 2020. Vaccines And Treatment Of COVID-19 . [online] Available at: <https://www.ecdc.europa.eu/en/covid-19/latest-evidence/vaccines-and-treatment> [Accessed 25 July 2020].
• Misra, S., 2012. Randomized double-blind placebo control studies, the "Gold Standard" in intervention-based studies. Indian Journal of Sexually Transmitted Diseases and AIDS , 33(2), pp. 131.
• The New York Times. 2021. Coronavirus Drug and Treatment Tracker [online] Available at https://www.nytimes.com/interactive/2020/science/coronavirus-drugs-treatments.html [Accessed 11 March 2020]
• The New York Times. 2021. Coronavirus Vaccine Tracker [online] Available at https://www.nytimes.com/interactive/2020/science/coronavirus-vaccine-tracker.html [Accessed 11 March 2020]
• Wang, Y., Zhang, D., Du, G., Du, R., Zhao, J., Jin, Y., Fu, S., Gao, L., Cheng, Z., Lu, Q., Hu, Y., Luo, G., Wang, K., Lu, Y., Li, H., Wang, S., Ruan, S., Yang, C., Mei, C., Wang, Y., Ding, D., Wu, F., Tang, X., Ye, X., Ye, Y., Liu, B., Yang, J., Yin, W., Wang, A., Fan, G., Zhou, F., Liu, Z., Gu, X., Xu, J., Shang, L., Zhang, Y., Cao, L., Guo, T., Wan, Y., Qin, H., Jiang, Y., Jaki, T., Hayden, F., Horby, P., Cao, B. and Wang, C., 2020. Remdesivir in adults with severe COVID-19: a randomized, double-blind, placebo-controlled, multicentre trial. The Lancet , 395(10236), pp. 1569-1578.
• Winchesterhospital.org. 2020. Double-Blind Study . [online] Available at: <https://www.winchesterhospital.org/health-library/article?id=21861> [Accessed 25 July 2020].
• WHO Ad Hoc Expert Group on the Next Steps for COVID-19 Evaluation. 2021. Placebo-Controlled Trials of Covid-19 Vaccines — Why We Still Need Them. N Engl J Med, 384:e2.

Last Updated: Mar 19, 2021

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• Double-blind experiment

Published November 23, 2021. Updated December 14, 2021.

A double-blind experiment is a preferred and reliable experimental technique designed to produce results with the least amount of bias. This implies that the results so obtained are less likely to be affected by variables unrelated to the experiment or the hypothesis being tested. Double-blind experiments are applied in the fields of medicine, psychology, and market research as well as many others.

Experimental research refers to a study that follows the requirements of scientific research design. It must include a hypothesis to be tested. Typically one or more variables are identified that can be influenced in the experiment by the researcher, and they are then measured, assessed, and compared. Most importantly, experimental research is executed in a controlled environment. In such cases, a blind experiment – single or a double – may be performed. A double-blind experiment is one in which neither the subjects nor the researcher know which study group (control or experimental) the subjects are placed in. A double-blind experiment facilitates a higher chance of bias removal than a single-blind experiment.

Control and experimental groups

Blind experiments are organized using control and experimental groups. The control group is the neutral group to which no treatment is provided, or no experimental condition is applied. The experimental group or the treatment group is subjected to the treatment or the experimental condition. For example, while testing the efficiency of an energy bar, the experimental group is given the energy bar and the control group is given a normal chocolate bar in similar packaging.

What is a blind experiment?

When non-blinded experiments are performed, the possibility of bias exists for both the participant and the researcher. Participants may display a placebo effect, i.e., they may modify their behavior or response to a study if they believe they have been placed in the experimental treatment group. They believe that they are part of a study and therefore should be affected by it. At the same time, the researcher may exhibit internal bias, i.e., they may construe observations that do not exist due to their existing beliefs. Hence, the need for blind experiments arises. A blind experiment is one in which the participants and coordinators of an experiment (subjects, researchers, and data analysts) are denied information that may influence them. A blind experiment allows for the reduction or elimination of experimental bias based on participants’ expectations, opinions, or preferences yielding accurate results.

Differences between a single-blind experiment and a double-blind experiment

In a single-blind experiment, only the subjects of an experiment are not told whether they are undergoing the control or the experimental treatment. In a double-blind experiment, both the participants and the researcher are unaware of which treatment group the participants are placed in.

In a single-blind experiment, even if the placebo effect is removed, the experimenter bias is not eliminated. For example, during the trial for an energy capsule, if the participants know they are being given the capsule they may perform better at more high-intensity activities because they received the medicine without it having any actual effect. Therefore, a single-blind technique is applied wherein the control group is given a placebo (or “sugar pill”) that is physically identical to the capsule given to the experimental group but lacks the medication being tested. In this way, the differing responses to the capsules can be recorded with higher accuracy. However, since the researchers are not blinded to any information, they may subconsciously influence the participants, show preferences during treatment distribution, overlook the results of the placebo control group, or over-read symptoms of a participant who has been administered the treatment.

In the case of a double-blind experiment, bias due to both the participant as well as the researcher is reduced. Returning to the previous example, if the researcher is unaware of the classification of the participants in the control vs. treatment groups, then the true effects of the placebo vs. treatment capsules would be obtained.

Why choose a double-blind experiment?

In the case of a double-blind experiment, both the researcher as well as the participants are blind to fundamental aspects of the study such as the hypothesis, expectations, and the assignment of the participants to the study groups. So researcher bias, observer bias, and confirmation bias can be minimized. This helps in obtaining improved and impartial results.

Applications of a double-blind experiment

Apart from having application in the field of medicine, double-blind experiments are used in other fields such as:

• Market research: During a taste test, a researcher may favor one pizza over another and make it more tempting to the participants than a competitor’s product. This would lead to invalid results. In such a scenario, the double-blind approach would yield more reliable results if the participants are given identical boxes containing either the researcher’s pizza or one from a competitor.
• Human behavior: While studying the effect of a new drink on the mood of participants, if the observer knows to which group the participant belongs then the attention paid to the participant’s behavior or the analysis of the behavior might be influenced. Using the double-blind method, experimenter bias would be reduced and the results would be more robust.

Key takeaways

• In a single-blind experiment, only the participants are unaware of their placement in the control or experimental group. In a double-blind experiment, the same information is withheld from both the researcher and participants.
• Double-blind experiments are better at eliminating bias in comparison to single-blind experiments.
• Both control and experimental groups are essential for the success of a blind experiment. The results of the control group act as a comparison standard for the results obtained from the experimental group.
• The placebo effect occurs when participants in a study believe they are given an experimental treatment when in fact they received a placebo, and this belief leads to a measurable positive effect.
• The outcome of an experiment can be influenced by the internal or experimenter’s bias, wherein judgments are made unconsciously on the basis of the researcher’s thoughts, past experiences, assumptions, beliefs, and/or understanding.
• Double-blind experiments are used in a variety of fields such as medicine, psychology, forensic science, law, and marketing to minimize experimenter bias.

Research Design

For more details, visit these additional research guides .

Research Variables

• Research design
• Research bias
• Type of variables
• Independent variable in research
• Dependent variables in research
• Confounding variables
• Control variables
• Extraneous variables

Experimental and Other Research Design

• Experimental research
• True experimental design
• Quasi-experimental design
• Between subject design
• Within subject design
• Case study research design
• Descriptive research design
• Longitudinal study
• Cross-sectional design
• Survey design
• Naturalistic observation
• Survey response scales
• Control group in science
• Null hypothesis

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16 Advantages and Disadvantages of a Double-Blind Study

A double-blind study uses a format where neither the participants nor the researchers know who receives a specific treatment. This procedure is useful because it prevents bias from forming in the achievable results. It is used most often when there is a direct need to understand the benefits of demand characteristics against the placebo effect.

What is unique about the placebo effect is that a person receives an inert substance that has no medical benefit. Participants believe that it is real medicine because a double-blind study wouldn’t inform anyone who gets the actual drug being studied. Researchers don’t receive that information either.

That means the results between the two groups can get compared to see if the effects of the drug are better than that of the placebo. It can also be a way to check for the development of side effects.

Several double-blind study advantages and disadvantages are worth reviewing when considering this format.

List of the Advantages of a Double-Blind Study

1. Three groups are typically part of a double-blind study. The typical double-blind study project will involve three groups of participants. You’ll have the treatment group, the placebo, group, and a control group. The first two receive the item in question based on their name, although only the administrator knows for certain who is getting what since researchers are kept in the dark. The control group doesn’t receive anything because it serves as the baseline against which the other two sets of results get compared.

When people in the placebo group improve more than the control group, then it shows a belief that the product works. If the treatment group shows better results than those who receive a placebo, then you know the medication worked.

2. It avoids deception in the research process. One of the criticized shortcomings of this approach is the fact that no one knows if the items they take or use is real or a placebo. The solution is to create two placebo subgroups where one is told that it is real medicine and the other is told it isn’t, which means researchers would need to deceive one set of participants. That process would violate the principles of informed consent.

The double-blind structure avoids this issue by providing complete information to all participants without letting on who receives the actual product getting studied.

3. It reduces the issue of experimenter bias. Using double-blind procedures can minimize the potential effects of research bias when collecting data. This issue often occurs when experimenters knowingly or unknowingly influence the results during information gathering or product administration during the project. There can also be subjective feelings that drive specific decisions that would occur if less information was present in the study.

By limiting the potential influences that could impact the collected data, the final results produced by the research or experiment has more validity.

4. The results of a double-blind project can get duplicated. One of the reasons why a double-blind study is considered a best practice is because the results offer the potential for duplication. Other researchers can follow the same protocols for administering placebos and the item being examined against a control group. If the results are similar, then it adds even more validity to the ability of a product or service to provide benefits. When duplication doesn’t happen, then the information from both studies can get compared to see what may have created a divergence in the data.

5. Double-blind assignment factors are randomized. No one knows who is going to be part of what group at the beginning of a double-blind study. The only participant group that knows they aren’t part of the placebo or target group are those who provide the control baselines. When looking at an intervention-based process, the fact that random assignment occurs for willing participants works to reduce the influence of confounding variables in the material.

6. High levels of control are part of the research process. The context of a double-blind research study allows administrators to manipulate variables so that the setting allows for direct observation. Control factors that could influence the environment can get added or removed to assist with the limitation of outside factors that would potentially change the data. This process allows for an accurate analysis of the collected data to ensure the authenticity of the results gets verified.

7. It is a process that’s usable in multiple industries. The double-blind study might be used primarily by the pharmaceutical industry because it can look directly at the impact of medication, but any field can use the processes to determine the validity of an idea. Agriculture, biology, chemistry, engineering, and social sciences all use these structures as a way to provide validation for a theory or idea.

List of the Disadvantages of a Double-Blind Study

1. It doesn’t reflect real-life circumstances. When a patient receives a pill after going to the doctor, they are told that the product is actual medicine intended to provide specific results. When participants receive something in a double-blind placebo study, then each person gets told explicitly that the item in question might be real medicine or a placebo. That leads to a different set of expectations that can influence the results of the work in adverse ways.

These artificial environments can cause an over-manipulation of the variables to produce circumstances that fall outside of the study’s parameters. When situations don’t feel realistic to a participant, then the quality of the data decreases exponentially.

2. Active placebos can interfere with the results. Double-blind studies respond to the objections of researchers unintentionally when communicating information about the results of a pill being authentic or a placebo. Objections to the pill offering this information don’t exist with this structure. Although both items look identical, the real medication provides biological effects. Even if the results aren’t measurable, the individuals can feel the impact of the medicine on their bodies.

This outcome may cause them to conclude that they are in the treatment group. That means some participants have a higher positive expectancy than those who don’t feel those effects. It is a disadvantage that can lead to a misinterpretation of the results being experienced in real-time.

3. It is not always possible to complete a double-blind study. There are times when a double-blind study is not possible. Any experiments that look at types of psychotherapy don’t benefit as an example because it would be impossible to keep participants in the dark about who receives treatment and who didn’t get the stated therapy. It only works when there is a way to provide two identical processes without clear communication about who receives the authentic item and who receives the placebo.

4. We do not fully understand the strength of the placebo effect. Research published by Science Translational Medicine in 2014 found that the simple act of taking a pill can establish a placebo effect for people. A migraine was being tested in this study. The control group took nothing, while the placebo group took a medication clearly labeled as “placebo.” Then one group took a migraine drug labeled with its name. Those who took the placebo had results that were 50% effective when reducing pain during a migraine effect.

The placebo effect can stimulate the brain into believing that the body is being healed, creating a natural mechanism that encourages better health. The presence of this effect doesn’t indicate the success or failure of a medication or another process in a double-blind study. It may be an indication that the group receiving the placebo has a powerful internal mechanism that provides self-healing.

5. Some people can have a negative response to a placebo. There can be times when an individual doesn’t have a response to the placebo at all. When that outcome occurs, then the effects of a process or medication can receive a direct comparison to see if the real product is useful. Some people can have an adverse reaction to the placebo, even producing unwanted side effects as if they were taking a real medication. It all depends on how each person feels.

A study involving people with asthma showed that using a placebo inhaler caused patients to do no better on breathing tests than sitting and doing nothing. When researchers asked how they felt about using the product, they reported that the placebo was just as effective as the regular medicine they used.

6. Randomization must use a structured process to be useful. The most common example of using randomization when assigning people to a group in a double-blind study is to flip a coin. It is an action that’s random and cannot be predicted, which means it is likely to be a 50/50 scenario over time as it gets tossed frequently. Assigning people who come to a specific location based on a day of the week can influence the results of the study unintentionally because there are other dynamics that control the behavior. That bias would be in the data without anyone recognizing its presence since it was placed there in the initial design.

7. Most double-blind studies are too small to provide a representative sample. Winchester Hospital, which is a division of Beth Israel Lahey Health in Massachusetts, says that a good double-blind study should enroll at least 100 individuals, “preferably as many as 300.” Effective treatments can prove themselves in small trials, but research requires more people to establish patterns so that results can be verified. Even when you have hundreds, or sometimes thousands, of participants in this work, the results might not extrapolate to the general population.

There were more than 4,100 trials in progress for pain treatments in 2011, but the only new approvals given were for formulations or updated dosages for existing medications. Even when drugs get into the third phase of testing, the product only has a 60% chance to continue moving forward. Divergent results often create failure.

8. It doesn’t work well for functional disorders. The highest response rates for a placebo occur when researchers are looking into functional disorders like Irritable Bowel Syndrome. It also happens when there are imprecise endpoint measurements, as with Crohn’s disease. People who have other immune-response conditions like rheumatoid arthritis. The FDA even notes that the placebo response is steadily growing in the general population.

This disadvantage creates another limitation where the structure of a double-blind study may not provide useful information.

9. Double-blind studies are an expensive effort to pursue. A double-blind study takes several months to complete so that researchers can look at each possible variable. It may be necessary to complete several efforts using different groups to collect enough data. When corporations look at the cost of these efforts, it can be an expense that reaches several million dollars before its completion. Government studies can quickly reach $1 billion or more, depending on the extent of the work and the industry or product under consideration.

When the Tufts Center for the Study of Drug Development looked at the cost of creating and bringing a new drug to the market, the expense was pegged at $2.6 billion. That’s why new prescription medicines are so expensive. Even the clinical trials for FDA approval have an average cost of $19 million.

Double-blind placebo studies are often called the gold standard for testing medications. This description is at its most powerful when studying new psychiatric medications since the placebo effect is a psychological benefit. It is a process that improves on the experiments that compare the response of someone taking a pill with those who do not.

Since no one knows who is getting what in a double-blind study, the danger of a researcher accidentally communicating non-verbally about the expectation of an item to work or not gets eliminated.

When reviewing these double-blind study advantages and disadvantages, the benefits that come from this process can only be achieved when structures that counter the potential negatives are in place. It gives us a baseline from which to work, but there are no guarantees that results are achievable.

Frequently asked questions

What is the difference between single-blind, double-blind and triple-blind studies.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalyzing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when: 

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
• Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Triangulation can help:

• Reduce research bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps: 

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

• Both variables are on an interval or ratio level of measurement
• Data from both variables follow normal distributions
• Your data have no outliers
• Your data is from a random or representative sample
• You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

• Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
• Experimenter effects : unintentional actions by researchers that influence study outcomes.
• Situational variables : environmental variables that alter participants’ behaviors.
• Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable .
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

• The type of soda – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Effect of a plant extract of fenugreek (Trigonella foenum-graecum) on testosterone in blood plasma and saliva in a double blind randomized controlled intervention study

Affiliations.

• 1 Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
• 2 Vitas AS, Oslo Science Park, Oslo, Norway.
• 3 DBG AS, Oslo Science Park, Oslo, Norway.
• 4 Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
• PMID: 39288153
• DOI: 10.1371/journal.pone.0310170

Many aging men experience reduced energy and libido related to non-optimal testosterone levels. We conducted a randomized double-blind trial with TrigozimR fenugreek extract to assess impact on plasma and saliva testosterone, and some subjective effects. 95 men (40-80y) completed a 12-week intervention, taking 3 tablets daily with 0 mg (placebo; n = 22), 600 mg (n = 21), 1200 mg (n = 25) and1800 mg (n = 27) fenugreek extract and essential nutrients. Samples were collected at weeks 0, 2, 6, and 12. Participants answered a pre- and post-intervention questionnaire on lifestyle and libido. We measured total testosterone (HPLC-MS/MS) and sex hormone binding globulin (ELISA), calculated free testosterone index (FTI), and measured saliva testosterone. Plasma total testosterone and FTI increased after any dose of TrigozimR vs. baseline (13.0%, p = 1.0x10-4 and 16.3%, p = 6.2x10-6), but not vs. placebo (9.0%, p = 0.122 and 11.3% p = 0.059). 1800 mg TrigozimR resulted in 12.2% increased FTI (p = 0.025). Saliva testosterone concentration increased after any dose of TrigozimR vs. baseline (31.1%, p = 2.3x10-4) and vs. placebo (37.2%, p = 0.042). 1800 mg TrigozimR for 12 weeks resulted in 19.6% (p = 0.006) increased saliva testosterone. Compliance was confirmed by enhanced plasma concentration of 25-hydroxy vitamin D3. We observed no subjective effects or side-effects of TrigozimR. TrigozimR increased saliva and plasma testosterone concentration during intervention but only for saliva vs. placebo. Saliva may be preferred for measuring free testosterone due to no protein-bound testosterone.

Copyright: © 2024 Lee-Ødegård et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

PubMed Disclaimer

Conflict of interest statement

TEG is CEO and stockowner in Vitas Ltd. CAD is stockowner, consultant, and board member in the contract laboratory Vitas Ltd where the intervention was performed and where most of the laboratory analyses were carried out. CAD is CEO and stockowner in the consulting company DBG Ltd responsible for design and execution. We received funding from the commercial source Vitas Ltd. This does not alter our adherence to PLOS ONE policies on sharing data and materials. SL was paid for performing the statistical analyses.

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Food & Function

A randomized, double-blind, placebo-controlled parallel trial to test the effect of inulin supplementation on migraine headache characteristics, quality of life and mental health symptoms in women with migraine.

* Corresponding authors

a Department of Community Nutrition, School of Nutrition and Food Science, Isfahan University of Medical Sciences, Isfahan, Iran E-mail: [email protected] Fax: +9837923232 Tel: +98 9132663418

b Neurology Research Center, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Migraine is a complex neurovascular disorder characterized by recurrent headache attacks that are often accompanied by symptoms such as vomiting, nausea, and sensitivity to sound or light. Preventing migraine attacks is highly important. Recent research has indicated that alterations in gut microbiota may influence the underlying mechanisms of migraines. This study aimed to investigate the effects of inulin supplementation on migraine headache characteristics, quality of life (QOL), and mental health symptoms in women with migraines. In a randomized double-blind placebo-controlled trial, 80 women with migraines aged 20 to 50 years were randomly assigned to receive 10 g day −1 of inulin or a placebo supplement for 12 weeks. Severity, frequency, and duration of migraine attacks, as well as depression, anxiety, stress, QOL, and headache impact test (HIT-6) scores, were examined at the start of the study and after 12 weeks of intervention. In this study, the primary outcome focused on the frequency of headache attacks, while secondary outcomes encompassed the duration and severity of headache attacks, QOL, and mental health. There was a significant reduction in severity (−1.95 vs. −0.84, P = 0.004), duration (−6.95 vs. −2.05, P = 0.023), frequency (−2.09 vs. −0.37, P < 0.001), and HIT-6 score (−10.30 vs. −6.52, P < 0.023) in the inulin group compared with the control. Inulin supplementation improved mental health symptoms, including depression (−4.47 vs. −1.45, P < 0.001), anxiety (−4.37 vs. −0.70, P < 0.001), and stress (−4.40 vs. −1.50, P < 0.001). However, no significant difference was observed between the two groups regarding changes in QOL score. This study provides evidence supporting the beneficial effects of inulin supplement on migraine symptoms and mental health status in women with migraines. Further studies are necessary to confirm these findings. Trial registration: Iranian Registry of Clinical Trials (https://www.irct.ir) (ID: IRCT20121216011763N58).

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M. Vajdi, F. Khorvash and G. Askari, Food Funct. , 2024, Advance Article , DOI: 10.1039/D4FO02796E

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