control in lab experiment

Home » experimental control important

What An Experimental Control Is And Why It’s So Important

Daniel Nelson

An experimental control is used in scientific experiments to minimize the effect of variables which are not the interest of the study. The control can be an object, population, or any other variable which a scientist would like to “control.”

You may have heard of experimental control, but what is it? Why is an experimental control important? The function of an experimental control is to hold constant the variables that an experimenter isn’t interested in measuring.

This helps scientists ensure that there have been no deviations in the environment of the experiment that could end up influencing the outcome of the experiment, besides the variable they are investigating. Let’s take a closer look at what this means.

You may have ended up here to understand why a control is important in an experiment. A control is important for an experiment because it allows the experiment to minimize the changes in all other variables except the one being tested.

To start with, it is important to define some terminology.

Terminology Of A Scientific Experiment


Negative	The negative control variable is a variable or group where no response is expected
Positive	A positive control is a group or variable that receives a treatment with a known positive result
Randomization	A randomized controlled seeks to reduce bias when testing a new treatment
Blind experiments	In blind experiments, the variable or group does not know the full amount of information about the trial to not skew results
Double-blind experiments	A double-blind group is where all parties do not know which individual is receiving the experimental treatment

Randomization is important as it allows for more non-biased results in experiments. Random numbers generators are often used both in scientific studies as well as on 지노 사이트 to make outcomes fairer.

Scientists use the scientific method to ask questions and come to conclusions about the nature of the world. After making an observation about some sort of phenomena they would like to investigate, a scientist asks what the cause of that phenomena could be. The scientist creates a hypothesis, a proposed explanation that answers the question they asked. A hypothesis doesn’t need to be correct, it just has to be testable.

The hypothesis is a prediction about what will happen during the experiment, and if the hypothesis is correct then the results of the experiment should align with the scientist’s prediction. If the results of the experiment do not align with the hypothesis, then a good scientist will take this data into consideration and form a new hypothesis that can better explain the phenomenon in question.

Independent and Dependent Variables

In order to form an effective hypothesis and do meaningful research, the researcher must define the experiment’s independent and dependent variables . The independent variable is the variable which the experimenter either manipulates or controls in an experiment to test the effects of this manipulation on the dependent variable. A dependent variable is a variable being measured to see if the manipulation has any effect.

Photo: frolicsomepl via Pixabay, CC0

For instance, if a researcher wanted to see how temperature impacts the behavior of a certain gas, the temperature they adjust would be the independent variable and the behavior of the gas the dependent variable.

Control Groups and Experimental Groups

There will frequently be two groups under observation in an experiment, the experimental group, and the control group . The control group is used to establish a baseline that the behavior of the experimental group can be compared to. If two groups of people were receiving an experimental treatment for a medical condition, one would be given the actual treatment (the experimental group) and one would typically be given a placebo or sugar pill (the control group).

Without an experimental control group, it is difficult to determine the effects of the independent variable on the dependent variable in an experiment. This is because there can always be outside factors that are influencing the behavior of the experimental group. The function of a control group is to act as a point of comparison, by attempting to ensure that the variable under examination (the impact of the medicine) is the thing responsible for creating the results of an experiment. The control group is holding other possible variables constant, such as the act of seeing a doctor and taking a pill, so only the medicine itself is being tested.

Why Are Experimental Controls So Important?

Experimental controls allow scientists to eliminate varying amounts of uncertainty in their experiments. Whenever a researcher does an experiment and wants to ensure that only the variable they are interested in changing is changing, they need to utilize experimental controls.

Experimental controls have been dubbed “controls” precisely because they allow researchers to control the variables they think might have an impact on the results of the study. If a researcher believes that some outside variables could influence the results of their research, they’ll use a control group to try and hold that thing constant and measure any possible influence it has on the results. It is important to note that there may be many different controls for an experiment, and the more complex a phenomenon under investigation is, the more controls it is likely to have.

Not only do controls establish a baseline that the results of an experiment can be compared to, they also allow researchers to correct for possible errors. If something goes wrong in the experiment, a scientist can check on the controls of the experiment to see if the error had to do with the controls. If so, they can correct this next time the experiment is done.

A Practical Example

Let’s take a look at a concrete example of experimental control. If an experimenter wanted to determine how different soil types impacted the germination period of seeds , they could set up four different pots. Each pot would be filled with a different soil type, planted with seeds, then watered and exposed to sunlight. Measurements would be taken regarding how long it took for the seeds to sprout in the different soil types.

Photo: Kaz via Pixabay, CC0

A control for this experiment might be to fill more pots with just the different types of soil and no seeds or to set aside some seeds in a pot with no soil. The goal is to try and determine that it isn’t something else other than the soil, like the nature of the seeds themselves, the amount of sun they were exposed to, or how much water they are given, that affected how quickly the seeds sprouted. The more variables a researcher controlled for, the surer they could be that it was the type of soil having an impact on the germination period.

Not All Experiments Are Controlled

“It doesn’t matter how beautiful your theory is, it doesn’t matter how smart you are. If it doesn’t agree with experiment, it’s wrong.” — Richard P. Feynman

While experimental controls are important , it is also important to remember that not all experiments are controlled. In the real world, there are going to be limitations on what variables a researcher can control for, and scientists often try to record as much data as they can during an experiment so they can compare factors and variables with one another to see if any variables they didn’t control for might have influenced the outcome. It’s still possible to draw useful data from experiments that don’t have controls, but it is much more difficult to draw meaningful conclusions based on uncontrolled data.

Though it is often impossible in the real world to control for every possible variable, experimental controls are an invaluable part of the scientific process and the more controls an experiment has the better off it is.

← Previous post

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

Knowledge Base
Methodology
Controlled Experiments | Methods & Examples of Control

Controlled Experiments | Methods & Examples of Control

Published on 19 April 2022 by Pritha Bhandari . Revised on 10 October 2022.

In experiments , researchers manipulate independent variables to test their effects on dependent variables. In a controlled experiment , all variables other than the independent variable are controlled or held constant so they don’t influence the dependent variable.

Controlling variables can involve:

Holding variables at a constant or restricted level (e.g., keeping room temperature fixed)
Measuring variables to statistically control for them in your analyses
Balancing variables across your experiment through randomisation (e.g., using a random order of tasks)

Why does control matter in experiments, methods of control, problems with controlled experiments, frequently asked questions about controlled experiments.

Control in experiments is critical for internal validity , which allows you to establish a cause-and-effect relationship between variables.

Your independent variable is the colour used in advertising.
Your dependent variable is the price that participants are willing to pay for a standard fast food meal.

Extraneous variables are factors that you’re not interested in studying, but that can still influence the dependent variable. For strong internal validity, you need to remove their effects from your experiment.

Design and description of the meal
Study environment (e.g., temperature or lighting)
Participant’s frequency of buying fast food
Participant’s familiarity with the specific fast food brand
Participant’s socioeconomic status

Prevent plagiarism, run a free check.

You can control some variables by standardising your data collection procedures. All participants should be tested in the same environment with identical materials. Only the independent variable (e.g., advert colour) should be systematically changed between groups.

Other extraneous variables can be controlled through your sampling procedures . Ideally, you’ll select a sample that’s representative of your target population by using relevant inclusion and exclusion criteria (e.g., including participants from a specific income bracket, and not including participants with colour blindness).

By measuring extraneous participant variables (e.g., age or gender) that may affect your experimental results, you can also include them in later analyses.

After gathering your participants, you’ll need to place them into groups to test different independent variable treatments. The types of groups and method of assigning participants to groups will help you implement control in your experiment.

Control groups

Controlled experiments require control groups . Control groups allow you to test a comparable treatment, no treatment, or a fake treatment, and compare the outcome with your experimental treatment.

You can assess whether it’s your treatment specifically that caused the outcomes, or whether time or any other treatment might have resulted in the same effects.

A control group that’s presented with red advertisements for a fast food meal
An experimental group that’s presented with green advertisements for the same fast food meal

Random assignment

To avoid systematic differences between the participants in your control and treatment groups, you should use random assignment .

This helps ensure that any extraneous participant variables are evenly distributed, allowing for a valid comparison between groups .

Random assignment is a hallmark of a ‘true experiment’ – it differentiates true experiments from quasi-experiments .

Masking (blinding)

Masking in experiments means hiding condition assignment from participants or researchers – or, in a double-blind study , from both. It’s often used in clinical studies that test new treatments or drugs.

Sometimes, researchers may unintentionally encourage participants to behave in ways that support their hypotheses. In other cases, cues in the study environment may signal the goal of the experiment to participants and influence their responses.

Using masking means that participants don’t know whether they’re in the control group or the experimental group. This helps you control biases from participants or researchers that could influence your study results.

Although controlled experiments are the strongest way to test causal relationships, they also involve some challenges.

Difficult to control all variables

Especially in research with human participants, it’s impossible to hold all extraneous variables constant, because every individual has different experiences that may influence their perception, attitudes, or behaviors.

But measuring or restricting extraneous variables allows you to limit their influence or statistically control for them in your study.

Risk of low external validity

Controlled experiments have disadvantages when it comes to external validity – the extent to which your results can be generalised to broad populations and settings.

The more controlled your experiment is, the less it resembles real world contexts. That makes it harder to apply your findings outside of a controlled setting.

There’s always a tradeoff between internal and external validity . It’s important to consider your research aims when deciding whether to prioritise control or generalisability in your experiment.

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

A testable hypothesis
One or more independent variables that you will manipulate
One or more dependent variables that you will measure

When designing the experiment, first decide:

How your variable(s) will be manipulated
How you will control for any potential confounding or lurking variables
How many subjects you will include
How you will assign treatments to your subjects

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Bhandari, P. (2022, October 10). Controlled Experiments | Methods & Examples of Control. Scribbr. Retrieved 9 September 2024, from https://www.scribbr.co.uk/research-methods/controlled-experiments/

Is this article helpful?

Pritha Bhandari

Microbe Notes

Controlled Experiments: Definition, Steps, Results, Uses

Controlled experiments ensure valid and reliable results by minimizing biases and controlling variables effectively.

Rigorous planning, ethical considerations, and precise data analysis are vital for successful experiment execution and meaningful conclusions.

Real-world applications demonstrate the practical impact of controlled experiments, guiding informed decision-making in diverse domains.

Controlled experiments are the systematic research method where variables are intentionally manipulated and controlled to observe the effects of a particular phenomenon. It aims to isolate and measure the impact of specific variables, ensuring a more accurate causality assessment.

Table of Contents

Interesting Science Videos

Importance of controlled experiments in various fields

Controlled experiments are significant across diverse fields, including science, psychology, economics, healthcare, and technology.

They provide a systematic approach to test hypotheses, establish cause-and-effect relationships, and validate the effectiveness of interventions or solutions.

Why Controlled Experiments Matter?

Validity and reliability of results.

Controlled experiments uphold the gold standard for scientific validity and reliability. By meticulously controlling variables and conditions, researchers can attribute observed outcomes accurately to the independent variable being tested. This precision ensures that the findings can be replicated and are trustworthy.

Minimizing Biases and Confounding Variables

One of the core benefits of controlled experiments lies in their ability to minimize biases and confounding variables. Extraneous factors that could distort results are mitigated through careful control and randomization. This enables researchers to isolate the effects of the independent variable, leading to a more accurate understanding of causality.

Achieving Causal Inference

Controlled experiments provide a strong foundation for establishing causal relationships between variables. Researchers can confidently infer causation by manipulating specific variables and observing resulting changes. The capability informs decision-making, policy formulation, and advancements across various fields.

Planning a Controlled Experiment

Formulating research questions and hypotheses.

Formulating clear research questions and hypotheses is paramount at the outset of a controlled experiment. These inquiries guide the direction of the study, defining the variables of interest and setting the stage for structured experimentation.

Well-defined questions and hypotheses contribute to focused research and facilitate meaningful data collection.

Identifying Variables and Control Groups

Identifying and defining independent, dependent, and control variables is fundamental to experimental planning.

Precise identification ensures that the experiment is designed to isolate the effect of the independent variable while controlling for other influential factors. Establishing control groups allows for meaningful comparisons and robust analysis of the experimental outcomes.

Designing Experimental Procedures and Protocols

Careful design of experimental procedures and protocols is essential for a successful controlled experiment. The step involves outlining the methodology, data collection techniques, and the sequence of activities in the experiment.

A well-designed experiment is structured to maintain consistency, control, and accuracy throughout the study, thereby enhancing the validity and credibility of the results.

Conducting a Controlled Experiment

Randomization and participant selection.

Randomization is a critical step in ensuring the fairness and validity of a controlled experiment. It involves assigning participants to different experimental conditions in a random and unbiased manner.

The selection of participants should accurately represent the target population, enhancing the results’ generalizability.

Data Collection Methods and Instruments

Selecting appropriate data collection methods and instruments is pivotal in gathering accurate and relevant data. Researchers often employ surveys, observations, interviews, or specialized tools to record and measure the variables of interest.

The chosen methods should align with the experiment’s objectives and provide reliable data for analysis.

Monitoring and Maintaining Experimental Conditions

Maintaining consistent and controlled experimental conditions throughout the study is essential. Regular monitoring helps ensure that variables remain constant and uncontaminated, reducing the risk of confounding factors.

Rigorous monitoring protocols and timely adjustments are crucial for the accuracy and reliability of the experiment.

Analysing Results and Drawing Conclusions

Data analysis techniques.

Data analysis involves employing appropriate statistical and analytical techniques to process the collected data. This step helps derive meaningful insights, identify patterns, and draw valid conclusions.

Common techniques include regression analysis, t-tests , ANOVA , and more, tailored to the research design and data type .

Interpretation of Results

Interpreting the results entails understanding the statistical outcomes and their implications for the research objectives.

Researchers analyze patterns, trends, and relationships revealed by the data analysis to infer the experiment’s impact on the variables under study. Clear and accurate interpretation is crucial for deriving actionable insights.

Implications and Potential Applications

Identifying the broader implications and potential applications of the experiment’s results is fundamental. Researchers consider how the findings can inform decision-making, policy development, or further research.

Understanding the practical implications helps bridge the gap between theoretical insights and real-world application.

Common Challenges and Solutions

Addressing ethical considerations.

Ethical challenges in controlled experiments include ensuring informed consent, protecting participants’ privacy, and minimizing harm.

Solutions involve thorough ethics reviews, transparent communication with participants, and implementing safeguards to uphold ethical standards throughout the experiment.

Dealing with Sample Size and Statistical Power

The sample size is crucial for achieving statistically significant results. Adequate sample sizes enhance the experiment’s power to detect meaningful effects accurately.

Statistical power analysis guides researchers in determining the optimal sample size for the experiment, minimizing the risk of type I and II errors .

Mitigating Unforeseen Variables

Unforeseen variables can introduce bias and affect the experiment’s validity. Researchers employ meticulous planning and robust control measures to minimize the impact of unforeseen variables.

Pre-testing and pilot studies help identify potential confounders, allowing researchers to adapt the experiment accordingly.

A controlled experiment involves meticulous planning, precise execution, and insightful analysis. Adhering to ethical standards, optimizing sample size, and adapting to unforeseen variables are key challenges that require thoughtful solutions.

Real-world applications showcase the transformative potential of controlled experiments across varied domains, emphasizing their indispensable role in evidence-based decision-making and progress.

https://www.khanacademy.org/science/biology/intro-to-biology/science-of-biology/a/experiments-and-observations
https://www.scribbr.com/methodology/controlled-experiment/
https://link.springer.com/10.1007/978-1-4899-7687-1_891
http://ai.stanford.edu/~ronnyk/GuideControlledExperiments.pdf
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6776925/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4017459/
https://www.merriam-webster.com/dictionary/controlled%20experiment

About Author

Krisha Karki

Why control an experiment?

John s torday.

1 Department of Pediatrics, Harbor‐UCLA Medical Center, Torrance, CA, USA

František Baluška

2 IZMB, University of Bonn, Bonn, Germany

Empirical research is based on observation and experimentation. Yet, experimental controls are essential for overcoming our sensory limits and generating reliable, unbiased and objective results.

An external file that holds a picture, illustration, etc.
Object name is EMBR-20-e49110-g001.jpg

We made a deliberate decision to become scientists and not philosophers, because science offers the opportunity to test ideas using the scientific method. And once we began our formal training as scientists, the greatest challenge beyond formulating a testable or refutable hypothesis was designing appropriate controls for an experiment. In theory, this seems trivial, but in practice, it is often difficult. But where and when did this concept of controlling an experiment start? It is largely attributed to Roger Bacon, who emphasized the use of artificial experiments to provide additional evidence for observations in his Novum Organum Scientiarum in 1620. Other philosophers took up the concept of empirical research: in 1877, Charles Peirce redefined the scientific method in The Fixation of Belief as the most efficient and reliable way to prove a hypothesis. In the 1930s, Karl Popper emphasized the necessity of refuting hypotheses in The Logic of Scientific Discoveries . While these influential works do not explicitly discuss controls as an integral part of experiments, their importance for generating solid and reliable results is nonetheless implicit.

… once we began our formal training as scientists, the greatest challenge beyond formulating a testable or refutable hypothesis was designing appropriate controls for an experiment.

But the scientific method based on experimentation and observation has come under criticism of late in light of the ever more complex problems faced in physics and biology. Chris Anderson, the editor of Wired Magazine, proposed that we should turn to statistical analysis, machine learning, and pattern recognition instead of creating and testing hypotheses, based on the Informatics credo that if you cannot answer the question, you need more data. However, this attitude subsumes that we already have enough data and that we just cannot make sense of it. This assumption is in direct conflict with David Bohm's thesis that there are two “Orders”, the Explicate and Implicate 1 . The Explicate Order is the way in which our subjective sensory systems perceive the world 2 . In contrast, Bohm's Implicate Order would represent the objective reality beyond our perception. This view—that we have only a subjective understanding of reality—dates back to Galileo Galilei who, in 1623, criticized the Aristotelian concept of absolute and objective qualities of our sensory perceptions 3 and to Plato's cave allegory that reality is only what our senses allow us to see.

The only way for systematically overcoming the limits of our sensory apparatus and to get a glimpse of the Implicate Order is through the scientific method, through hypothesis‐testing, controlled experimentation. Beyond the methodology, controlling an experiment is critically important to ensure that the observed results are not just random events; they help scientists to distinguish between the “signal” and the background “noise” that are inherent in natural and living systems. For example, the detection method for the recent discovery of gravitational waves used four‐dimensional reference points to factor out the background noise of the Cosmos. Controls also help to account for errors and variability in the experimental setup and measuring tools: The negative control of an enzyme assay, for instance, tests for any unrelated background signals from the assay or measurement. In short, controls are essential for the unbiased, objective observation and measurement of the dependent variable in response to the experimental setup.

The only way for systematically overcoming the limits of our sensory apparatus […] is through the Scientific Method, through hypothesis‐testing, controlled experimentation.

Nominally, both positive and negative controls are material and procedural; that is, they control for variability of the experimental materials and the procedure itself. But beyond the practical issues to avoid procedural and material artifacts, there is an underlying philosophical question. The need for experimental controls is a subliminal recognition of the relative and subjective nature of the Explicate Order. It requires controls as “reference points” in order to transcend it, and to approximate the Implicate Order.

This is similar to Peter Rowlands’ 4 dictum that everything in the Universe adds up to zero, the universal attractor in mathematics. Prior to the introduction of zero, mathematics lacked an absolute reference point similar to a negative or positive control in an experiment. The same is true of biology, where the cell is the reference point owing to its negative entropy: It appears as an attractor for the energy of its environment. Hence, there is a need for careful controls in biology: The homeostatic balance that is inherent to life varies during the course of an experiment and therefore must be precisely controlled to distinguish noise from signal and approximate the Implicate Order of life.

P < 0.05 tacitly acknowledges the explicate order

Another example of the “subjectivity” of our perception is the level of accuracy we accept for differences between groups. For example, when we use statistical methods to determine if an observed difference between control and experimental groups is a random occurrence or a specific effect, we conventionally consider a p value of less than or equal to 5% as statistically significant; that is, there is a less than 0.05 probability that the effect is random. The efficacy of this arbitrary convention has been debated for decades; suffice to say that despite questioning the validity of that convention, a P value of < 0.05 reflects our acceptance of the subjectivity of our perception of reality.

… controls are essential for the unbiased, objective observation and measurement of the dependent variable in response to the experimental setup.

Thus, if we do away with hypothesis‐testing science in favor of informatics based on data and statistics—referring to Anderson's suggestion—it reflects our acceptance of the noise in the system. However, mere data analysis without any underlying hypothesis is tantamount to “garbage in‐garbage out”, in contrast to well‐controlled imaginative experiments to separate the wheat from the chaff. Albert Einstein was quoted as saying that imagination was more important than knowledge.

The ultimate purpose of the scientific method is to understand ourselves and our place in Nature. Conventionally, we subscribe to the Anthropic Principle, that we are “in” this Universe, whereas the Endosymbiosis Theory, advocated by Lynn Margulis, stipulates that we are “of” this Universe as a result of the assimilation of the physical environment. According to this theory, the organism endogenizes external factors to make them physiologically “useful”, such as iron as the core of the hemoglobin molecule, or ancient bacteria as mitochondria.

… there is a fundamental difference between knowing via believing and knowing based on empirical research.

By applying the developmental mechanism of cell–cell communication to phylogeny, we have revealed the interrelationships between cells and explained evolution from its origin as the unicellular state to multicellularity via cell–cell communication. The ultimate outcome of this research is that consciousness is the product of cellular processes and cell–cell communication in order to react to the environment and better anticipate future events 5 , 6 . Consciousness is an essential prerequisite for transcending the Explicate Order toward the Implicate Order via cellular sensory and cognitive systems that feed an ever‐expanding organismal knowledge about both the environment and itself.

It is here where the empirical approach to understanding nature comes in with its emphasis that knowledge comes only from sensual experience rather than innate ideas or traditions. In the context of the cell or higher systems, knowledge about the environment can only be gained by sensing and analyzing the environment. Empiricism is similar to an equation in which the variables and terms form a product, or a chemical reaction, or a biological process where the substrates, aka sensory data, form products, that is, knowledge. However, it requires another step—imagination, according to Albert Einstein—to transcend the Explicate Order in order to gain insight into the Implicate Order. Take for instance, Dmitri Ivanovich Mendeleev's Periodic Table of Elements: his brilliant insight was not just to use Atomic Number to organize it, but also to consider the chemical reactivities of the Elements by sorting them into columns. By introducing chemical reactivity to the Periodic Table, Mendeleev provided something like the “fourth wall” in Drama, which gives the audience an omniscient, god‐like perspective on what is happening on stage.

The capacity to transcend the subjective Explicate Order to approximate the objective Implicate Order is not unlike Eastern philosophies like Buddhism or Taoism, which were practiced long before the scientific method. An Indian philosopher once pointed out that the Hindus have known for 30,000 years that the Earth revolves around the sun, while the Europeans only realized this a few hundred years ago based on the work of Copernicus, Brahe, and Galileo. However, there is a fundamental difference between knowing via believing and knowing based on empirical research. A similar example is Aristotle's refusal to test whether a large stone would fall faster than a small one, as he knew the answer already 7 . Galileo eventually performed the experiment from the Leaning Tower in Pisa to demonstrate that the fall time of two objects is independent of their mass—which disproved Aristotle's theory of gravity that stipulated that objects fall at a speed proportional to their mass. Again, it demonstrates the power of empiricism and experimentation as formulated by Francis Bacon, John Locke, and others, over intuition and rationalizing.

Even if our scientific instruments provide us with objective data, we still need to apply our consciousness to evaluate and interpret such data.

Following the evolution from the unicellular state to multicellular organisms—and reverse‐engineering it to a minimal‐cell state—reveals that biologic diversity is an artifact of the Explicate Order. Indeed, the unicell seems to be the primary level of selection in the Implicate Order, as it remains proximate to the First Principles of Physiology, namely negative entropy (negentropy), chemiosmosis, and homeostasis. The first two principles are necessary for growth and proliferation, whereas the last reflects Newton's Third Law of Motion that every action has an equal and opposite reaction so as to maintain homeostasis.

All organisms interact with their surroundings and assimilate their experience as epigenetic marks. Such marks extend to the DNA of germ cells and thus change the phenotypic expression of the offspring. The offspring, in turn, interacts with the environment in response to such epigenetic modifications, giving rise to the concept of the phenotype as an agent that actively and purposefully interacts with its environment in order to adapt and survive. This concept of phenotype based on agency linked to the Explicate Order fundamentally differs from its conventional description as a mere set of biologic characteristics. Organisms’ capacities to anticipate future stress situations from past memories are obvious in simple animals such as nematodes, as well as in plants and bacteria 8 , suggesting that the subjective Explicate Order controls both organismal behavior and trans‐generational evolution.

That perspective offers insight to the nature of consciousness: not as a “mind” that is separate from a “body”, but as an endogenization of physical matter, which complies with the Laws of Nature. In other words, consciousness is the physiologic manifestation of endogenized physical surroundings, compartmentalized, and made essential for all organisms by forming the basis for their physiology. Endocytosis and endocytic/synaptic vesicles contribute to endogenization of cellular surroundings, allowing eukaryotic organisms to gain knowledge about the environment. This is true not only for neurons in brains, but also for all eukaryotic cells 5 .

Such a view of consciousness offers insight to our awareness of our physical surroundings as the basis for self‐referential self‐organization. But this is predicated on our capacity to “experiment” with our environment. The burgeoning idea that we are entering the Anthropocene, a man‐made world founded on subjective senses instead of Natural Laws, is a dangerous step away from our innate evolutionary arc. Relying on just our senses and emotions, without experimentation and controls to understand the Implicate Order behind reality, is not just an abandonment of the principles of the Enlightenment, but also endangers the planet and its diversity of life.

Acknowledgements

John Torday has been a recipient of NIH Grant HL055268. František Baluška is thankful to numerous colleagues for very stimulating discussions on topics analyzed in this article.

EMBO Reports (2019) 20 : e49110 [ PMC free article ] [ PubMed ] [ Google Scholar ]

Contributor Information

John S Torday, Email: ude.alcu@yadrotj .

František Baluška, Email: ed.nnob-inu@aksulab .

COVID-19 Tracker
Biochemistry
Anatomy & Physiology
Microbiology
Neuroscience
Animal Kingdom
NGSS High School
Latest News
Editors’ Picks
Weekly Digest
Quotes about Biology

Controlled Experiment

Reviewed by: BD Editors

Controlled Experiment Definition

A controlled experiment is a scientific test that is directly manipulated by a scientist, in order to test a single variable at a time. The variable being tested is the independent variable , and is adjusted to see the effects on the system being studied. The controlled variables are held constant to minimize or stabilize their effects on the subject. In biology, a controlled experiment often includes restricting the environment of the organism being studied. This is necessary to minimize the random effects of the environment and the many variables that exist in the wild.

In a controlled experiment, the study population is often divided into two groups. One group receives a change in a certain variable, while the other group receives a standard environment and conditions. This group is referred to as the control group , and allows for comparison with the other group, known as the experimental group . Many types of controls exist in various experiments, which are designed to ensure that the experiment worked, and to have a basis for comparison. In science, results are only accepted if it can be shown that they are statistically significant . Statisticians can use the difference between the control group and experimental group and the expected difference to determine if the experiment supports the hypothesis , or if the data was simply created by chance.

Examples of Controlled Experiment

Music preference in dogs.

Do dogs have a taste in music? You might have considered this, and science has too. Believe it or not, researchers have actually tested dog’s reactions to various music genres. To set up a controlled experiment like this, scientists had to consider the many variables that affect each dog during testing. The environment the dog is in when listening to music, the volume of the music, the presence of humans, and even the temperature were all variables that the researches had to consider.

In this case, the genre of the music was the independent variable. In other words, to see if dog’s change their behavior in response to different kinds of music, a controlled experiment had to limit the interaction of the other variables on the dogs. Usually, an experiment like this is carried out in the same location, with the same lighting, furniture, and conditions every time. This ensures that the dogs are not changing their behavior in response to the room. To make sure the dogs don’t react to humans or simply the noise of the music, no one else can be in the room and the music must be played at the same volume for each genre. Scientist will develop protocols for their experiment, which will ensure that many other variables are controlled.

This experiment could also split the dogs into two groups, only testing music on one group. The control group would be used to set a baseline behavior, and see how dogs behaved without music. The other group could then be observed and the differences in the group’s behavior could be analyzed. By rating behaviors on a quantitative scale, statistics can be used to analyze the difference in behavior, and see if it was large enough to be considered significant. This basic experiment was carried out on a large number of dogs, analyzing their behavior with a variety of different music genres. It was found that dogs do show more relaxed and calm behaviors when a specific type of music plays. Come to find out, dogs enjoy reggae the most.

Scurvy in Sailors

In the early 1700s, the world was a rapidly expanding place. Ships were being built and sent all over the world, carrying thousands and thousands of sailors. These sailors were mostly fed the cheapest diets possible, not only because it decreased the costs of goods, but also because fresh food is very hard to keep at sea. Today, we understand that lack of essential vitamins and nutrients can lead to severe deficiencies that manifest as disease. One of these diseases is scurvy.

Scurvy is caused by a simple vitamin C deficiency, but the effects can be brutal. Although early symptoms just include general feeling of weakness, the continued lack of vitamin C will lead to a breakdown of the blood cells and vessels that carry the blood. This results in blood leaking from the vessels. Eventually, people bleed to death internally and die. Before controlled experiments were commonplace, a simple physician decided to tackle the problem of scurvy. James Lind, of the Royal Navy, came up with a simple controlled experiment to find the best cure for scurvy.

He separated sailors with scurvy into various groups. He subjected them to the same controlled condition and gave them the same diet, except one item. Each group was subjected to a different treatment or remedy, taken with their food. Some of these remedies included barley water, cider and a regiment of oranges and lemons. This created the first clinical trial , or test of the effectiveness of certain treatments in a controlled experiment. Lind found that the oranges and lemons helped the sailors recover fast, and within a few years the Royal Navy had developed protocols for growing small leafy greens that contained high amounts of vitamin C to feed their sailors.

Related Biology Terms

Field Experiment – An experiment conducted in nature, outside the bounds of total control.
Independent Variable – The thing in an experiment being changed or manipulated by the experimenter to see effects on the subject.
Controlled Variable – A thing that is normalized or standardized across an experiment, to remove it from having an effect on the subject being studied.
Control Group – A group of subjects in an experiment that receive no independent variable, or a normalized amount, to provide comparison.

Cite This Article

Subscribe to our newsletter, privacy policy, terms of service, scholarship, latest posts, white blood cell, t cell immunity, satellite cells, embryonic stem cells, popular topics, endocrine system, natural selection, homeostasis, pituitary gland.

What Is a Controlled Experiment?

Definition and Example

Scientific Method
Chemical Laws
Periodic Table
Projects & Experiments
Biochemistry
Physical Chemistry
Medical Chemistry
Chemistry In Everyday Life
Famous Chemists
Activities for Kids
Abbreviations & Acronyms
Weather & Climate
Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
B.A., Physics and Mathematics, Hastings College

A controlled experiment is one in which everything is held constant except for one variable . Usually, a set of data is taken to be a control group , which is commonly the normal or usual state, and one or more other groups are examined where all conditions are identical to the control group and to each other except for one variable.

Sometimes it's necessary to change more than one variable, but all of the other experimental conditions will be controlled so that only the variables being examined change. And what is measured is the variables' amount or the way in which they change.

Controlled Experiment

A controlled experiment is simply an experiment in which all factors are held constant except for one: the independent variable.
A common type of controlled experiment compares a control group against an experimental group. All variables are identical between the two groups except for the factor being tested.
The advantage of a controlled experiment is that it is easier to eliminate uncertainty about the significance of the results.

Example of a Controlled Experiment

Let's say you want to know if the type of soil affects how long it takes a seed to germinate, and you decide to set up a controlled experiment to answer the question. You might take five identical pots, fill each with a different type of soil, plant identical bean seeds in each pot, place the pots in a sunny window, water them equally, and measure how long it takes for the seeds in each pot to sprout.

This is a controlled experiment because your goal is to keep every variable constant except the type of soil you use. You control these features.

Why Controlled Experiments Are Important

The big advantage of a controlled experiment is that you can eliminate much of the uncertainty about your results. If you couldn't control each variable, you might end up with a confusing outcome.

For example, if you planted different types of seeds in each of the pots, trying to determine if soil type affected germination, you might find some types of seeds germinate faster than others. You wouldn't be able to say, with any degree of certainty, that the rate of germination was due to the type of soil. It might as well have been due to the type of seeds.

Or, if you had placed some pots in a sunny window and some in the shade or watered some pots more than others, you could get mixed results. The value of a controlled experiment is that it yields a high degree of confidence in the outcome. You know which variable caused or did not cause a change.

Are All Experiments Controlled?

No, they are not. It's still possible to obtain useful data from uncontrolled experiments, but it's harder to draw conclusions based on the data.

An example of an area where controlled experiments are difficult is human testing. Say you want to know if a new diet pill helps with weight loss. You can collect a sample of people, give each of them the pill, and measure their weight. You can try to control as many variables as possible, such as how much exercise they get or how many calories they eat.

However, you will have several uncontrolled variables, which may include age, gender, genetic predisposition toward a high or low metabolism, how overweight they were before starting the test, whether they inadvertently eat something that interacts with the drug, etc.

Scientists try to record as much data as possible when conducting uncontrolled experiments, so they can see additional factors that may be affecting their results. Although it is harder to draw conclusions from uncontrolled experiments, new patterns often emerge that would not have been observable in a controlled experiment.

For example, you may notice the diet drug seems to work for female subjects, but not for male subjects, and this may lead to further experimentation and a possible breakthrough. If you had only been able to perform a controlled experiment, perhaps on male clones alone, you would have missed this connection.

Box, George E. P., et al. Statistics for Experimenters: Design, Innovation, and Discovery . Wiley-Interscience, a John Wiley & Soncs, Inc., Publication, 2005.
Creswell, John W. Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research . Pearson/Merrill Prentice Hall, 2008.
Pronzato, L. "Optimal experimental design and some related control problems". Automatica . 2008.
Robbins, H. "Some Aspects of the Sequential Design of Experiments". Bulletin of the American Mathematical Society . 1952.
Understanding Simple vs Controlled Experiments
What Is the Difference Between a Control Variable and Control Group?
The Role of a Controlled Variable in an Experiment
Scientific Variable
DRY MIX Experiment Variables Acronym
Six Steps of the Scientific Method
Scientific Method Vocabulary Terms
What Are the Elements of a Good Hypothesis?
Scientific Method Flow Chart
What Is an Experimental Constant?
Scientific Hypothesis Examples
What Are Examples of a Hypothesis?
What Is a Hypothesis? (Science)
Null Hypothesis Examples
What Is a Testable Hypothesis?
Random Error vs. Systematic Error

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

Knowledge Base

Methodology

Control Variables | What Are They & Why Do They Matter?

Control Variables | What Are They & Why Do They Matter?

Published on March 1, 2021 by Pritha Bhandari . Revised on June 22, 2023.

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

Variables may be controlled directly by holding them constant throughout a study (e.g., by controlling the room temperature in an experiment), or they may be controlled indirectly through methods like randomization or statistical control (e.g., to account for participant characteristics like age in statistical tests). Control variables can help prevent research biases like omitted variable bias from affecting your results.

Examples of control variables
Research question	Control variables
Does soil quality affect plant growth?
Does caffeine improve memory recall?
Do people with a fear of spiders perceive spider images faster than other people?

Why do control variables matter, how do you control a variable, control variable vs. control group, other interesting articles, frequently asked questions about control variables.

Control variables enhance the internal validity of a study by limiting the influence of confounding and other extraneous variables . This helps you establish a correlational or causal relationship between your variables of interest and helps avoid research bias .

Aside from the independent and dependent variables , all variables that can impact the results should be controlled. If you don’t control relevant variables, you may not be able to demonstrate that they didn’t influence your results. Uncontrolled variables are alternative explanations for your results and affect the reliability of your arguments.

Control variables in experiments

In an experiment , a researcher is interested in understanding the effect of an independent variable on a dependent variable. Control variables help you ensure that your results are solely caused by your experimental manipulation.

The independent variable is whether the vitamin D supplement is added to a diet, and the dependent variable is the level of alertness.

To make sure any change in alertness is caused by the vitamin D supplement and not by other factors, you control these variables that might affect alertness:

Timing of meals
Caffeine intake
Screen time

Control variables in non-experimental research

In an observational study or other types of non-experimental research, a researcher can’t manipulate the independent variable (often due to practical or ethical considerations ). Instead, control variables are measured and taken into account to infer relationships between the main variables of interest.

To account for other factors that are likely to influence the results, you also measure these control variables:

Marital status

Prevent plagiarism. Run a free check.

There are several ways to control extraneous variables in experimental designs, and some of these can also be used in observational studies or quasi-experimental designs.

Random assignment

In experimental studies with multiple groups, participants should be randomly assigned to the different conditions. Random assignment helps you balance the characteristics of groups so that there are no systematic differences between them.

This method of assignment controls participant variables that might otherwise differ between groups and skew your results.

It’s possible that the participants who found the study through Facebook use more screen time during the day, and this might influence how alert they are in your study.

Standardized procedures

It’s important to use the same procedures across all groups in an experiment. The groups should only differ in the independent variable manipulation so that you can isolate its effect on the dependent variable (the results).

To control variables , you can hold them constant at a fixed level using a protocol that you design and use for all participant sessions. For example, the instructions and time spent on an experimental task should be the same for all participants in a laboratory setting.

To control for diet, fresh and frozen meals are delivered to participants three times a day.
To control meal timings, participants are instructed to eat breakfast at 9:30, lunch at 13:00, and dinner at 18:30.
To control caffeine intake, participants are asked to consume a maximum of one cup of coffee a day.

Statistical controls

You can measure and control for extraneous variables statistically to remove their effects on other types of variables .

“Controlling for a variable” means modelling 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.

A control variable isn’t the same as a control group . Control variables are held constant or measured throughout a study for both control and experimental groups, while an independent variable varies between control and experimental groups.

A control group doesn’t undergo the experimental treatment of interest, and its outcomes are compared with those of the experimental group. A control group usually has either no treatment, a standard treatment that’s already widely used, or a placebo (a fake treatment).

Aside from the experimental treatment, everything else in an experimental procedure should be the same between an experimental and control group.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Normal distribution
Degrees of freedom
Null hypothesis
Discourse analysis
Control groups
Mixed methods research
Non-probability sampling
Quantitative research
Ecological validity

Research bias

Rosenthal effect
Implicit bias
Cognitive bias
Selection bias
Negativity bias
Status quo bias

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

Academic style
Vague sentences
Style consistency

See an example

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.

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 .

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.

“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.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bhandari, P. (2023, June 22). Control Variables | What Are They & Why Do They Matter?. Scribbr. Retrieved September 9, 2024, from https://www.scribbr.com/methodology/control-variable/

Is this article helpful?

Pritha Bhandari

Other students also liked, what is a controlled experiment | definitions & examples, independent vs. dependent variables | definition & examples, extraneous variables | examples, types & controls, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

Science Notes Posts
Contact Science Notes
Todd Helmenstine Biography
Anne Helmenstine Biography
Free Printable Periodic Tables (PDF and PNG)
Periodic Table Wallpapers
Interactive Periodic Table
Periodic Table Posters
Science Experiments for Kids
How to Grow Crystals
Chemistry Projects
Fire and Flames Projects
Holiday Science
Chemistry Problems With Answers
Physics Problems
Unit Conversion Example Problems
Chemistry Worksheets
Biology Worksheets
Periodic Table Worksheets
Physical Science Worksheets
Science Lab Worksheets
My Amazon Books

Control Group Definition and Examples

The control group is the set of subjects that does not receive the treatment in a study. In other words, it is the group where the independent variable is held constant. This is important because the control group is a baseline for measuring the effects of a treatment in an experiment or study. A controlled experiment is one which includes one or more control groups.

The experimental group experiences a treatment or change in the independent variable. In contrast, the independent variable is constant in the control group.
A control group is important because it allows meaningful comparison. The researcher compares the experimental group to it to assess whether or not there is a relationship between the independent and dependent variable and the magnitude of the effect.
There are different types of control groups. A controlled experiment has one more control group.

Control Group vs Experimental Group

The only difference between the control group and experimental group is that subjects in the experimental group receive the treatment being studied, while participants in the control group do not. Otherwise, all other variables between the two groups are the same.

Control Group vs Control Variable

A control group is not the same thing as a control variable. A control variable or controlled variable is any factor that is held constant during an experiment. Examples of common control variables include temperature, duration, and sample size. The control variables are the same for both the control and experimental groups.

Types of Control Groups

There are different types of control groups:

Placebo group : A placebo group receives a placebo , which is a fake treatment that resembles the treatment in every respect except for the active ingredient. Both the placebo and treatment may contain inactive ingredients that produce side effects. Without a placebo group, these effects might be attributed to the treatment.
Positive control group : A positive control group has conditions that guarantee a positive test result. The positive control group demonstrates an experiment is capable of producing a positive result. Positive controls help researchers identify problems with an experiment.
Negative control group : A negative control group consists of subjects that are not exposed to a treatment. For example, in an experiment looking at the effect of fertilizer on plant growth, the negative control group receives no fertilizer.
Natural control group : A natural control group usually is a set of subjects who naturally differ from the experimental group. For example, if you compare the effects of a treatment on women who have had children, the natural control group includes women who have not had children. Non-smokers are a natural control group in comparison to smokers.
Randomized control group : The subjects in a randomized control group are randomly selected from a larger pool of subjects. Often, subjects are randomly assigned to either the control or experimental group. Randomization reduces bias in an experiment. There are different methods of randomly assigning test subjects.

Control Group Examples

Here are some examples of different control groups in action:

Negative Control and Placebo Group

For example, consider a study of a new cancer drug. The experimental group receives the drug. The placebo group receives a placebo, which contains the same ingredients as the drug formulation, minus the active ingredient. The negative control group receives no treatment. The reason for including the negative group is because the placebo group experiences some level of placebo effect, which is a response to experiencing some form of false treatment.

Positive and Negative Controls

For example, consider an experiment looking at whether a new drug kills bacteria. The experimental group exposes bacterial cultures to the drug. If the group survives, the drug is ineffective. If the group dies, the drug is effective.

The positive control group has a culture of bacteria that carry a drug resistance gene. If the bacteria survive drug exposure (as intended), then it shows the growth medium and conditions allow bacterial growth. If the positive control group dies, it indicates a problem with the experimental conditions. A negative control group of bacteria lacking drug resistance should die. If the negative control group survives, something is wrong with the experimental conditions.

Bailey, R. A. (2008). Design of Comparative Experiments . Cambridge University Press. ISBN 978-0-521-68357-9.
Chaplin, S. (2006). “The placebo response: an important part of treatment”. Prescriber . 17 (5): 16–22. doi: 10.1002/psb.344
Hinkelmann, Klaus; Kempthorne, Oscar (2008). Design and Analysis of Experiments, Volume I: Introduction to Experimental Design (2nd ed.). Wiley. ISBN 978-0-471-72756-9.
Pithon, M.M. (2013). “Importance of the control group in scientific research.” Dental Press J Orthod . 18 (6):13-14. doi: 10.1590/s2176-94512013000600003
Stigler, Stephen M. (1992). “A Historical View of Statistical Concepts in Psychology and Educational Research”. American Journal of Education . 101 (1): 60–70. doi: 10.1086/444032

Positive Control vs Negative Control: Differences & Examples

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

Learn about our Editorial Process

positive control vs negative control, explained below

A positive control is designed to confirm a known response in an experimental design , while a negative control ensures there’s no effect, serving as a baseline for comparison.

The two terms are defined as below:

Positive control refers to a group in an experiment that receives a procedure or treatment known to produce a positive result. It serves the purpose of affirming the experiment’s capability to produce a positive outcome.
Negative control refers to a group that does not receive the procedure or treatment and is expected not to yield a positive result. Its role is to ensure that a positive result in the experiment is due to the treatment or procedure.

The experimental group is then compared to these control groups, which can help demonstrate efficacy of the experimental treatment in comparison to the positive and negative controls.

Positive Control vs Negative Control: Key Terms

Control groups.

A control group serves as a benchmark in an experiment. Typically, it is a subset of participants, subjects, or samples that do not receive the experimental treatment (as in negative control).

This could mean assigning a placebo to a human subject or leaving a sample unaltered in chemical experiments. By comparing the results obtained from the experimental group to the control, you can ascertain whether any differences are due to the treatment or random variability.

A well-configured experimental control is critical for drawing valid conclusions from an experiment. Correct use of control groups permits specificity of findings, ensuring the integrity of experimental data.

See More: Control Variables Examples

The Negative Control

Negative control is a group or condition in an experiment that ought to show no effect from the treatment.

It is useful in ensuring that the outcome isn’t accidental or influenced by an external cause. Imagine a medical test, for instance. You use distilled water, anticipating no reaction, as a negative control.

If a significant result occurs, it warns you of a possible contamination or malfunction during the testing. Failure of negative controls to stay ‘negative’ risks misinterpretation of the experiment’s result, and could undermine the validity of the findings.

The Positive Control

A positive control, on the other hand, affirms an experiment’s functionality by demonstrating a known reaction.

This might be a group or condition where the expected output is known to occur, which you include to ensure that the experiment can produce positive results when they are present. For instance, in testing an antibiotic, a well-known pathogen, susceptible to the medicine, could be the positive control.

Positive controls affirm that under appropriate conditions your experiment can produce a result. Without this reference, experiments could fail to detect true positive results, leading to false negatives. These two controls, used judiciously, are backbones of effective experimental practice.

Experimental Groups

Experimental groups are primarily characterized by their exposure to the examined variable.

That is, these are the test subjects that receive the treatment or intervention under investigation. The performance of the experimental group is then compared against the well-established markers – our positive and negative controls.

For example, an experimental group may consist of rats undergoing a pharmaceutical testing regime, or students learning under a new educational method. Fundamentally, this unit bears the brunt of the investigation and their response powers the outcomes.

However, without positive and negative controls, gauging the results of the experimental group could become erratic. Both control groups exist to highlight what outcomes are expected with and without the application of the variable in question. By comparing results, a clearer connection between the experiment variables and the observed changes surfaces, creating robust and indicative scientific conclusions.

Positive and Negative Control Examples

1. a comparative study of old and new pesticides’ effectiveness.

This hypothetical study aims to evaluate the effectiveness of a new pesticide by comparing its pest-killing potential with old pesticides and an untreated set. The investigation involves three groups: an untouched space (negative control), another treated with an established pesticide believed to kill pests (positive control), and a third area sprayed with the new pesticide (experimental group).

Negative Control: This group consists of a plot of land infested by pests and not subjected to any pesticide treatment. It acts as the negative control. You expect no decline in pest populations in this area. Any unexpected decrease could signal external influences (i.e. confounding variables ) on the pests unrelated to pesticides, affecting the experiment’s validity.
Positive Control: Another similar plot, this time treated with a well-established pesticide known to reduce pest populations, constitutes the positive control. A significant reduction in pests in this area would affirm that the experimental conditions are conducive to detect pest-killing effects when a pesticide is applied.
Experimental Group: This group consists of the third plot impregnated with the new pesticide. Carefully monitoring the pest level in this research area against the backdrop of the control groups will reveal whether the new pesticide is effective or not. Through comparison with the other groups, any difference observed can be attributed to the new pesticide.

2. Evaluating the Effectiveness of a Newly Developed Weight Loss Pill

In this hypothetical study, the effectiveness of a newly formulated weight loss pill is scrutinized. The study involves three groups: a negative control group given a placebo with no weight-reducing effect, a positive control group provided with an approved weight loss pill known to cause a decrease in weight, and an experimental group given the newly developed pill.

Negative Control: The negative control is comprised of participants who receive a placebo with no known weight loss effect. A significant reduction in weight in this group would indicate confounding factors such as dietary changes or increased physical activity, which may invalidate the study’s results.
Positive Control: Participants in the positive control group receive an FDA-approved weight loss pill, anticipated to induce weight loss. The success of this control would prove that the experiment conditions are apt to detect the effects of weight loss pills.
Experimental Group: This group contains individuals receiving the newly developed weight loss pill. Comparing the weight change in this group against both the positive and negative control, any difference observed would offer evidence about the effectiveness of the new pill.

3. Testing the Efficiency of a New Solar Panel Design

This hypothetical study focuses on assessing the efficiency of a new solar panel design. The study involves three sets of panels: a set that is shaded to yield no solar energy (negative control), a set with traditional solar panels that are known to produce an expected level of solar energy (positive control), and a set fitted with the new solar panel design (experimental group).

Negative Control: The negative control involves a set of solar panels that are deliberately shaded, thus expecting no solar energy output. Any unexpected energy output from this group could point towards measurement errors, needed to be rectified for a valid experiment.
Positive Control: The positive control set up involves traditional solar panels known to produce a specific amount of energy. If these panels produce the expected energy, it validates that the experiment conditions are capable of measuring solar energy effectively.
Experimental Group: The experimental group features the new solar panel design. By comparing the energy output from this group against both the controls, any significant output variation would indicate the efficiency of the new design.

4. Investigating the Efficacy of a New Fertilizer on Plant Growth

This hypothetical study investigates the efficacy of a newly formulated fertilizer on plant growth. The study involves three sets of plants: a set without any fertilizer (negative control), a set treated with an established fertilizer known to promote plant growth (positive control), and a third set fed with the new fertilizer (experimental group).

Negative Control: The negative control involves a set of plants not receiving any fertilizer. Lack of significant growth in this group will confirm that any observed growth in other groups is due to the applied fertilizer rather than other uncontrolled factors.
Positive Control: The positive control involves another set of plants treated with a well-known fertilizer, expected to promote plant growth. Adequate growth in these plants will validate that the experimental conditions are suitable to detect the influence of a good fertilizer on plant growth.
Experimental Group: The experimental group consists of the plants subjected to the newly formulated fertilizer. Investigating the growth in this group against the growth in the control groups will provide ascertained evidence whether the new fertilizer is efficient or not.

5. Evaluating the Impact of a New Teaching Method on Student Performance

This hypothetical study aims to evaluate the impact of a new teaching method on students’ performance. This study involves three groups, a group of students taught through traditional methods (negative control), another group taught through an established effective teaching strategy (positive control), and one more group of students taught through the new teaching method (experimental group).

Negative Control: The negative control comprises students taught by standard teaching methods, where you expect satisfactory but not top-performing results. Any unexpected high results in this group could signal external factors such as private tutoring or independent study, which in turn may distort the experimental outcome.
Positive Control: The positive control consists of students taught by a known efficient teaching strategy. High performance in this group would prove that the experimental conditions are competent to detect the efficiency of a teaching method.
Experimental Group: This group consists of students receiving instruction via the new teaching method. By analyzing their performance against both control groups, any difference in results could be attributed to the new teaching method, determining its efficacy.

Table Summary

Aspect	Positive Control	Negative Control
	To confirm that the experiment is working properly and that results can be detected.	To ensure that there is no effect when there shouldn’t be, and to provide a baseline for comparison.
	A known effect or change.	No effect or change.
	Used to demonstrate that the experimental setup can produce a positive result.	Used to demonstrate that any observed effects are due to the experimental treatment and not other factors.
	Plants given known amounts of sunlight to ensure they grow.	Plants given no sunlight to ensure they don’t grow.
	A substrate known to be acted upon by the enzyme.	A substrate that the enzyme doesn’t act upon.
	A medium known to support bacterial growth.	A medium that doesn’t support bacterial growth (sterile medium).
	Validates that the experimental system is sensitive and can detect changes if they occur.	Validates that observed effects are due to the variable being tested and not due to external or unknown factors.
	If the positive control doesn’t produce the expected result, the experimental setup or procedure may be flawed.	If the negative control shows an effect, there may be contamination or other unexpected variables influencing the results.

Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 10 Reasons you’re Perpetually Single
Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 20 Montessori Toddler Bedrooms (Design Inspiration)
Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 21 Montessori Homeschool Setups
Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 101 Hidden Talents Examples

Control Group vs Experimental Group

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

Learn about our Editorial Process

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

In a controlled experiment , scientists compare a control group, and an experimental group is identical in all respects except for one difference – experimental manipulation.

Differences

Unlike the experimental group, the control group is not exposed to the independent variable under investigation. So, it provides a baseline against which any changes in the experimental group can be compared.

Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

Almost all experimental studies are designed to include a control group and one or more experimental groups. In most cases, participants are randomly assigned to either a control or experimental group.

Because participants are randomly assigned to either group, we can assume that the groups are identical except for manipulating the independent variable in the experimental group.

It is important that every aspect of the experimental environment is the same and that the experimenters carry out the exact same procedures with both groups so researchers can confidently conclude that any differences between groups are actually due to the difference in treatments.

Control Group

A control group consists of participants who do not receive any experimental treatment. The control participants serve as a comparison group.

The control group is matched as closely as possible to the experimental group, including age, gender, social class, ethnicity, etc.

The difference between the control and experimental groups is that the control group is not exposed to the independent variable , which is thought to be the cause of the behavior being investigated.

Researchers will compare the individuals in the control group to those in the experimental group to isolate the independent variable and examine its impact.

The control group is important because it serves as a baseline, enabling researchers to see what impact changes to the independent variable produce and strengthening researchers’ ability to draw conclusions from a study.

Without the presence of a control group, a researcher cannot determine whether a particular treatment truly has an effect on an experimental group.

Control groups are critical to the scientific method as they help ensure the internal validity of a study.

Assume you want to test a new medication for ADHD . One group would receive the new medication, and the other group would receive a pill that looked exactly the same as the one that the others received, but it would be a placebo. The group that takes the placebo would be the control group.

Types of Control Groups

Positive control group.

A positive control group is an experimental control that will produce a known response or the desired effect.
A positive control is used to ensure a test’s success and confirm an experiment’s validity.
For example, when testing for a new medication, an already commercially available medication could serve as the positive control.

Negative Control Group

A negative control group is an experimental control that does not result in the desired outcome of the experiment.
A negative control is used to ensure that there is no response to the treatment and help identify the influence of external factors on the test.
An example of a negative control would be using a placebo when testing for a new medication.

Experimental Group

An experimental group consists of participants exposed to a particular manipulation of the independent variable. These are the participants who receive the treatment of interest.

Researchers will compare the responses of the experimental group to those of a control group to see if the independent variable impacted the participants.

An experiment must have at least one control group and one experimental group; however, a single experiment can include multiple experimental groups, which are all compared against the control group.

Having multiple experimental groups enables researchers to vary different levels of an experimental variable and compare the effects of these changes to the control group and among each other.

Assume you want to study to determine if listening to different types of music can help with focus while studying.

You randomly assign participants to one of three groups: one group that listens to music with lyrics, one group that listens to music without lyrics, and another group that listens to no music.

The group of participants listening to no music while studying is the control group, and the groups listening to music, whether with or without lyrics, are the two experimental groups.

Frequently Asked Questions

1. what is the difference between the control group and the experimental group in an experimental study.

Put simply; an experimental group is a group that receives the variable, or treatment, that the researchers are testing, whereas the control group does not. These two groups should be identical in all other aspects.

2. What is the purpose of a control group in an experiment

A control group is essential in experimental research because it:

Provides a baseline against which the effects of the manipulated variable (the independent variable) can be measured.

Helps to ensure that any changes observed in the experimental group are indeed due to the manipulation of the independent variable and not due to other extraneous or confounding factors.

Helps to account for the placebo effect, where participants’ beliefs about the treatment can influence their behavior or responses.

In essence, it increases the internal validity of the results and the confidence we can have in the conclusions.

3. Do experimental studies always need a control group?

Not all experiments require a control group, but a true “controlled experiment” does require at least one control group. For example, experiments that use a within-subjects design do not have a control group.

In within-subjects designs , all participants experience every condition and are tested before and after being exposed to treatment.

These experimental designs tend to have weaker internal validity as it is more difficult for a researcher to be confident that the outcome was caused by the experimental treatment and not by a confounding variable.

4. Can a study include more than one control group?

Yes, studies can include multiple control groups. For example, if several distinct groups of subjects do not receive the treatment, these would be the control groups.

5. How is the control group treated differently from the experimental groups?

The control group and the experimental group(s) are treated identically except for one key difference: exposure to the independent variable, which is the factor being tested. The experimental group is subjected to the independent variable, whereas the control group is not.

This distinction allows researchers to measure the effect of the independent variable on the experimental group by comparing it to the control group, which serves as a baseline or standard.

Bailey, R. A. (2008). Design of Comparative Experiments. Cambridge University Press. ISBN 978-0-521-68357-9.

Hinkelmann, Klaus; Kempthorne, Oscar (2008). Design and Analysis of Experiments, Volume I: Introduction to Experimental Design (2nd ed.). Wiley. ISBN 978-0-471-72756-9.

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

1.4 Designed Experiments

Observational studies vs. experiments.

Ignoring anecdotal evidence, there are two primary types of data collection: observational studies and controlled (designed) experiments . Remember, we typically cannot make claims of causality from observation studies because of the potential presence of confounding factors. However, making causal conclusions based on experiments is often reasonable if we control for those factors.

Suppose you want to investigate the effectiveness of vitamin D in preventing disease. You recruit a group of subjects and ask them if they regularly take vitamin D. You notice that the subjects who take vitamin D exhibit better health on average than those who do not. Does this prove that vitamin D is effective in disease prevention? It does not. There are many differences between the two groups beyond just vitamin D consumption. People who take vitamin D regularly often take other steps to improve their health: exercise, diet, other vitamin supplements, choosing not to smoke. Any one of these factors could influence health. As described, this study does not necessarily prove that vitamin D is the key to disease prevention.

Experiments ultimately aim to provide evidence for use in decision-making, so how could we narrow our focus and make claims of causality? In this section, you will learn important aspects of experimental design.

Designed Experiments

The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory variable . The affected variable is called the response variable . In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The different values of the explanatory variable may be called treatments . An experimental unit is a single object or individual being measured.

The main principles to follow in experimental design are:

Randomization

Replication.

In order to provide evidence that the explanatory variable is indeed causing the changes in the response variable, it is necessary to isolate the explanatory variable. The researcher must design the experiment in such a way that there is only one difference between groups being compared: the planned treatments. This is accomplished by randomizing the experimental units placed into treatment groups. When subjects are assigned treatments randomly, all of the potential lurking variables are spread equally among the groups. At this point, the only difference between groups is the one imposed by the researcher. As a result, different outcomes measured in the response variable must be a direct result of the different treatments. In this way, an experiment can show an apparent cause-and-effect connection between the explanatory and response variables.

Recall our previous example of investigating the effectiveness of vitamin D in preventing disease. Individuals in our trial could be randomly assigned, perhaps by flipping a coin, into one of two groups: the control group (no treatment) and the experimental group (extra doses of vitamin D).

The more cases researchers observe, the more accurately they can estimate the effect of the explanatory variable on the response. In a single study, we replicate by collecting a sufficiently large sample. Additionally, a group of scientists may replicate an entire study to verify an earlier finding. It is also helpful to subject individuals to the same treatment more than once, which is known as repeated measures .

The power of suggestion can have an important influence on the outcome of an experiment. Studies have shown that the expectations of the study participant can be as important as the actual medication. In one study of performance-enhancing drugs, researchers noted, “ Results showed that believing one had taken the substance resulted in [performance] times almost as fast as those associated with consuming the drug itself. In contrast, taking the drug without knowledge yielded no significant performance increment.” [1]

It is often difficult to isolate the effects of the explanatory variable. To counter the power of suggestion, researchers set aside one treatment group as a control group . This group is given a placebo treatment—a treatment that cannot influence the response variable. The control group helps researchers balance the effects of being in an experiment with the effects of the active treatments. Of course, if you are participating in a study and you know that you are receiving a pill that contains no actual medication, then the power of suggestion is no longer a factor. Blinding in a randomized experiment preserves the power of suggestion. When a person involved in a research study is blinded, he does not know who is receiving the active treatment(s) and who is receiving the placebo treatment. A double-blind experiment is one in which both the subjects and the researchers involved with the subjects are unaware.

Randomized experiments are an essential tool in research. The U.S. Food and Drug Administration typically requires that a new drug can only be marketed after two independently conducted randomized trials confirm its safety and efficacy; the European Medicines Agency has a similar policy. Large randomized experiments in medicine have provided the basis for major public health initiatives. In 1954, approximately 750,000 children participated in a randomized study comparing the polio vaccine with a placebo. In the United States, the results of the study quickly led to the widespread and successful use of the vaccine for polio prevention.

How does sleep deprivation affect your ability to drive? A recent study measured its effects on 19 professional drivers. Each driver participated in two experimental sessions: one after normal sleep and one after 27 hours of total sleep deprivation. The treatments were assigned in random order. In each session, performance was measured on a variety of tasks including a driving simulation.

The Smell & Taste Treatment and Research Foundation conducted a study to investigate whether smell can affect learning. Subjects completed pencil-and-paper mazes multiple times while wearing masks. They completed the mazes three times wearing floral-scented masks and three times with unscented masks. Participants were assigned at random to wear the floral mask during either the first three or last three trials. For each trial, researchers recorded the time it took to complete the maze and whether the subject’s impression of the mask’s scent was positive, negative, or neutral.

More Experimental Design

There are many different experimental designs from the most basic—a single treatment and control group—to some very complicated designs. When working with more than one treatment in an experimental design setting, these variables are often called factors , especially if they are categorical. The values of factors are are often called levels . When there are multiple factors, the combinations of each of the levels are called treatment combinations , or interactions. Some basic types of interactions you may see are:

Completely randomized
Block design
Matched pairs design

Completely Randomized

This essential research tool does not require much explanation. It involves figuring out how many treatments will be administered and randomly assigning participants to their respective groups.

Block Design

Researchers sometimes know or suspect that variables outside of the treatment influence the response. Based on this, they may first group individuals into blocks and then randomly draw cases from each block for the treatment groups. This strategy is often referred to as blocking . For instance, if we are looking at the effect of a drug on heart attacks, we might first split patients in the study into low-risk and high-risk blocks, then randomly assign half the patients from each block to the control group and the other half to the treatment group, as shown in the figure below. This strategy ensures each treatment group has an equal number of low-risk and high-risk patients.

Box labeled 'numbered patients' that has 54 blue or orange circles numbered from 1-54. Two arrows point from this box to 2 boxes below it with the caption 'create blocks'. The left box is all of the oragne cirlces grouped toegether labeled 'low-risk patients'. The right box is all of the blue circles grouped together labeled 'high-risk patients'. An arrow points down from the left box and the right box with the caption 'randomly split in half'. The arrows point to a 'Control' box and a 'Treatment' box. Both of these boxes have half orange circles and half blue circles.

Matched Pairs

A matched pairs design is one where very similar individuals (or even the same individual) receive two different treatments (or treatment vs. control) and the results are compared. Though this design is very effective, it can be hard to find many suitably similar individuals. Some common forms of a matched pairs design are twin studies, before-and-after measurements, pre- and post-test situations, and crossover studies.

Was the use of a new wetsuit design responsible for an observed increase in swim velocities at the 2000 Summer Olympics? In a matched pairs study designed to investigate this question, twelve competitive swimmers swam 1,500 meters at maximal speed, once wearing a wetsuit and once wearing a regular swimsuit. The order of wetsuit and swimsuit trials was randomized for each of the 12 swimmers. Table 1.1 shows the average velocity recorded for each swimmer, measured in meters per second (m/s).

Table 1.1: Average velocity of swimmers
	swimmer.number	wet.suit.velocity	swim.suit.velocity	velocity.diff
1	1	1.57	1.49	0.08
2	2	1.47	1.37	0.10
3	3	1.42	1.35	0.07
4	4	1.35	1.27	0.08
5	5	1.22	1.12	0.10
6	6	1.75	1.64	0.11
7	7	1.64	1.59	0.05
8	8	1.57	1.52	0.05
9	9	1.56	1.50	0.06
10	10	1.53	1.45	0.08
11	11	1.49	1.44	0.05
12	12	1.51	1.41	0.10

In this data, two sets of observations are uniquely paired so that an observation in one set matches an observation in the other; in this case, each swimmer has two measured velocities, one with a wetsuit and one with a swimsuit. A natural measure of the effect of the wetsuit on swim velocity is the difference between the measured maximum velocities (velocity.diff = wet.suit.velocit – swim.suit.velocity). Even though there are two measurements per individual, using the difference in observations as the variable of interest allows for the problem to be analyzed.

A new windshield treatment claims to repel water more effectively. Ten windshields are tested by simulating rain without the new treatment. The same windshields are then treated, and the experiment is run again. What experiment design is being implemented here?

A new medicine is said to help improve sleep. Eight subjects are picked at random and given the medicine. The mean hours slept for each person were recorded before and after stating the medication. What experiment design is being implemented here?

Click here for more multimedia resources, including podcasts, videos, lecture notes, and worked examples.

Figure References

Figure 1.5: Kindred Grey (2020). “Block design.” CC BY-SA 4.0.

McClung, Mary, and Dave Collins, ""Because I know it will!": Placebo Effects of an Ergogenic Acid on Athletic Performance," Journal of Sport & Exercise Psychology, 29 , no. 3 (2007): 382-394. ↵

Data collection where no variables are manipulated

Type of experiment where variables are manipulated and data is collected in a controlled setting

The independent variable in an experiment; the value controlled by researchers

The dependent variable in an experiment; the value that is measured for change at the end of an experiment

Different values or components of the explanatory variable applied in an experiment

Any individual or object to be measured

When an individual goes through a single treatment more than once

A group in a randomized experiment that receives no (or inactive) treatment but is otherwise managed exactly as the other groups

An inactive treatment that has no real effect on the explanatory variable

Not telling participants which treatment they are receiving

The act of blinding both the subjects of an experiment and the researchers who work with the subjects

Variables in an experiment

Certain values of variables in an experiment

Combinations of levels of variables in an experiment

Dividing participants into treatment groups randomly

Grouping individuals based on a variable into "blocks" and then randomizing cases within each block to the treatment groups

Very similar individuals (or even the same individual) receive two different treatments (or treatment vs. control), then the results are compared

Significant Statistics Copyright © 2024 by John Morgan Russell, OpenStaxCollege, OpenIntro is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

What’s unique about the CDAO’s upcoming Global Information Dominance Experiment

By Brandi Vincent

September 12, 2024

The Pentagon’s next Global Information Dominance Experiment — GIDE 12 — will put the minimum viable capability for Combined Joint All-Domain Command and Control that officials have been collaboratively refining in recent years via this rapid experimentation series, to its most international test yet.

Roots of the Defense Department’s GIDE series trace back to 2020, but in 2022, it was revamped when Deputy Defense Secretary Kathleen Hicks tasked the Chief Digital and AI Office with strategically enabling technologies that could help realize the U.S. military’s nascent CJADC2 warfighting construct through the initiative. Each GIDE event is now leading up to a worldwide, joint activity where U.S. combatant commands and multiple international military partners collaboratively are expected to unleash next-generation command-and-control capabilities in late 2025.

“We’re in the middle of planning GIDE 12 right now, which is about 45 days away,” Col. Matthew Strohmeyer, an Air Force pilot and senior CDAO official overseeing the rapid experimentation effort, told DefenseScoop Thursday.

During a panel at a GDIT event produced by FedScoop in collaboration with AWS, Strohmeyer shed light on what his team has been learning in the latest GIDE iterations and shared new details about what to expect in the upcoming event.

“We’ve certainly had international partners join for the past several [experiments]. But for this one, we are going to be significantly expanding that,” he said.

During the last few iterations, the U.S. military’s Five Eyes partners — Canada, Australia, New Zealand and the U.K. — had to “watch from a distance,” Strohmeyer noted, due to what he said were technical challenges associated with sharing data.

But for GIDE 12, “we’re going to be able to technically integrate much more than we’ve ever been able to in the past — and, sometimes in an autonomous way, be able to share data between us, which we’ve not been able to do in the past. So, that’s exciting for us,” he said.

Broadly, this year his team has three “mission threads” they’re pursuing with GIDE.

“The first one is ‘global integration,’ or the ability for the Joint Staff and our allies and partners to see the world collaboratively and make decisions much more quickly than we were able to make in the past — and rather than a regional way, in a truly global way and truly digitized way,” Strohmeyer told DefenseScoop.

The second mission thread involves helping the military services enable joint kill chains, and the third encompasses driving improvements with allied and partner data-sharing.

It’s all meant to enable the minimum viable capability (MVC) they’ve developed for CJADC2 so far, which Strohmeyer said is performant but not yet perfect.

“Our goal is to significantly expand that out for GIDE 12, and also test it in a really robust operational environment to allow us to be able to see, can we achieve true global integration with some of those allies and partners against a very robust mission set, and can we actually do it in a way that’s performing and that’s ready for [current] operations?” Strohmeyer said.

He confirmed that several elements that contribute to the MVC have been fielded and are already “being used in real-world operations right now.”

Though he didn’t name the combatant commands participating or locations where it’ll all unfold, Strohmeyer repeatedly emphasized how the next GIDE will push that MVC further and be more global in nature than those that came before .

A key focus of GIDE 12, he noted, involves “a horrible military acronym” known as C5P — or Cross Combatant Command and Coalition Cooperative Planning.

“Essentially, it’s taking what is currently hundreds of people, sometimes thousands, and sometimes hundreds of thousands of man-hours, to be able to collaborate and come up with a slide that says, ‘This is what we’re going to do’ — and instead create a truly digital, in-minutes collaboration that can allow us to be able to get in in advance of our adversaries and hopefully deter a fight from happening. So that’s what we hope to do,” Strohmeyer told DefenseScoop.

More Like This

Pentagon using ai to modernize legacy code, anduril reveals new family of software-defined, autonomous cruise missiles, space force awards follow-on contract to scale terrestrial data transport system, top stories, after nearly 5 years of litigation, navy awards next system in advanced airborne electronic attack, watchdogs move to evaluate nga’s maven integration, army units able to communicate more dispersed in recent exercise, parallel tracks aim to improve current army c2 while pursuing long-term options, air force integrating amraam weapons onto first batch of cca drones, more scoops.

What’s unique about DIU’s new prize challenge to extend the Pentagon’s drone supply

Key to the Pentagon’s concept for modern war is standardization

How the Pentagon will advance its CJADC2 experiments — with allies — next year

Pentagon poised to launch inaugural ‘challenge’ for global information dominance experiments, pentagon getting ready to onboard new vendors and applications for cjadc2 tech, task force lima preps new space for generative ai experimentation, what’s next for the us military’s global information dominance experiments, latest podcasts.

How the Navy is reducing workforce friction to improve mission outcomes

How DARPA is looking to AI to fend off cyber vulnerabilities through a challenge program

How the DOD protects national security interests by monitoring climate change

Unlocking the potential of AI with a modern data strategy

Marine expeditionary force to get autonomous ‘narco boat' for experimentation
Army, AeroVironment ink nearly $1B contract for Switchblade killer drones
Senate appropriators recommend ‘full funding’ for Replicator — and potentially even more money
USAFE commander expects new drones to ‘change quite a few things’ for Air Force readiness
DOD putting final touches on new zero trust 'assessment standard'
New assistant secretary of defense wants to focus on return on investment for cyber operations
Air Force splitting up intelligence and cyber effects organization
Cybercom chief to Congress: Let us finish our study before directing an examination for a Cyber Force
Army implements generative AI platform to cArmy cloud environment
New DOD supercomputer designed to thwart chem and bio threats
NGA to offer $700M for data-labeling to advance computer vision models
NGA launches new pilot program to standardize computer vision model accreditation
DIU seeks data mesh solution to unify distribution across DOD networks
DISA mulls adding vendors, different contract types for JWCC 2.0
Navy’s Project Overmatch steams ahead at RIMPAC
DISA’s overseas cloud efforts gain JOEmentum in Europe

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

View all journals
Explore content
About the journal
Publish with us
Sign up for alerts
Open access
Published: 14 September 2024

Cobalt-catalyzed hydrothiolation of alkynes for the diverse synthesis of branched alkenyl sulfides

Jiale Ying 1 ,
Yan Tan 2 &
Zhan Lu ORCID: orcid.org/0000-0002-3069-079X 1 , 2

Nature Communications volume 15 , Article number: 8057 ( 2024 ) Cite this article

Metrics details

Homogeneous catalysis
Synthetic chemistry methodology

Alkenyl sulfides have gained increasing prominence in medicinal chemistry and materials. Hydrothiolation of alkynes for the diverse synthesis of alkenyl sulfides is an appealing method. Herein, we report a cobalt-catalyzed Markovnikov hydromethylthiolation of alkynes to afford branched alkenyl methylsulfanes with good yields and high regioselectivity. This method also enables the diverse synthesis of branched alkenyl sulfides. The reaction shows good functional group tolerance and could be scaled up. The mechanistic studies including control experiments, deuterium-labeling experiments, and Hammett plot indicated alkynes insertion followed by electrophilic thiolation pathway.

Introduction

Alkenyl sulfides have important applications in antibiotic drugs, analytical detection, and functional materials (Fig. 1A ), driving continuous developments in synthetic methods for their preparation 1 , 2 , 3 , 4 , 5 . The use of readily available and cost-effective alkynes as starting materials for hydrothiolation represents an appealing method (Fig. 1B ) 6 , 7 , 8 , 9 , 10 . It should be noted that the synthetic methods for branched configuration products are still limited. Representative examples include transition metal-catalysis, such as Pd 11 , 12 , 13 , 14 , 15 , Rh 16 , 17 , 18 , 19 , 20 , Th 21 , 22 , Zr 23 , Ni 24 , 25 , 26 , 27 , and photocatalysis 28 , using thiols or thiophenols as raw materials. Although the methylthio group is recognized for its utility in modifying drug properties by enhancing solubility and altering metabolic pathways 4 , 29 , 30 , the hydromethylthiolation of alkynes with methanethiol is rarely explored 31 , 32 , 33 , due to the high toxicity, low boiling point (279 K at 1 atm), repugnant odor, and strong coordination with transition metal catalysts (as an example, the bond energy of Pd–SMe (92–99 kcal/mol) is much higher than that of C Ar –SMe (82–85 kcal/mol), making the reduction elimination process difficult) 34 . Furthermore, hydrothiolation reactions are significantly influenced by the groups of alkynes and sulfur sources 14 , and there is still no protocol applicable to four types of hydrothiolation (reactions between alkyl, aryl alkynes and alkyl, aryl sulfur sources), so the development of more versatile protocols for the diversity hydrothiolation of alkynes remains desirable.

A Alkenyl-thio moiety in drug candidates and materials. B Catalytic hydrothiolation of terminal alkynes. C This work: Markovnikov hydrothiolation of terminal alkynes.

While significant progress has been made in developing masked sulfurizing reagents for the green synthesis of sulfur-containing compounds 31 , 35 , 36 , 37 , 38 , 39 , to the best of our knowledge, metal-catalyzed regioselective hydrothiolation of alkynes with masked sulfurization reagents has not been reported. The intrinsic challenge of regioselective hydrothiolation involves addressing the issues of chemoselectivity (the masked sulfurizing reagents could be reduced to deliver side products, such as thiols and disulfides), regioselectivity (branched versus linear alkenyl sulfides), and potential metal poisoning 40 , 41 . Here, we report a cobalt-catalyzed Markovnikov hydromethylthiolation of alkynes with masked sulfurizing reagents, which also enables diverse reactions between alkyl, aryl alkynes and alkyl, aryl sulfur sources (Fig. 1C ).

Reaction optimization

The investigation was initiated by examining the hydromethylthiolation of 1-ethynyl-4-methoxybenzene ( 1a ) with sulfur electrophile ( 2a ). After screening various masked sulfurizing reagents, it was observed that using L1 as the ligand, OMTS (1,1,1,3,5,7,7,7-octamethyltetrasiloxane) as the silane, and lithium methoxide as the base, 3a could be obtained with excellent yield (90%) and excellent regioselectivity ( b / l = 97/3) (Table 1 , entry 1). However, with phthalimide, methanethio, or methanesulfonyl masked sulfurizing reagents, the reactions afforded 3a in 5–15% yields (entries 2–4), demonstrating the substantial influence of the masks. Employing L2 and L3 as ligands although the yields were reduced, products are predominantly afforded in the branched configuration; however, the bisphosphine ligand L4 did not facilitate this reaction (entries 5–7). Other bases such as sodium methoxide, lithium tert -butoxide or CsF significantly reduced the yield of 3a (entries 11–13). Finally, the pre-catalyst complex was used instead of the in situ catalyst to control the reaction, and the similar yield and selectivity of 3a were obtained (entry 14).

Substrate scope

After optimizing the reaction conditions, a series of alkynes as substrates were investigated (Fig. 2 ). Due to the instability of branched alkenyl sulfides in air, all separation steps were carried out in a nitrogen atmosphere whenever possible. The electron-donating and electron-withdrawing groups on the phenyl ring were tolerated to afford 3b - 3k in 50–83% yields with 90/10– > 99/1 b / l . Particularly, C sp 2 –Br ( 3d ), C sp 2 –I ( 3e ) and phenylamino ( 3k ) were well compatible, providing more chances for further molecule cross-linkage. Meta -substituted and ortho -substituted alkynes 1l – 1o could also participate in the reaction to afford 3l– 3o in 49–85% yields with 92/8–97/3 b / l . The alkynes containing a polycyclic ring or heterocycle, such as piperonyl ( 1p ), 2-naphthyl ( 1q ), and 3-thienyl ( 1r ), could be converted to the corresponding products 3p – 3r in 54–77% yields with 95/5–97/3 b / l . Notably, aliphatic alkynes were also applicable to the reaction. Ethynylcyclohexane ( 1 s ) could participate to deliver the hydromethylthiolation product 3 s in 51% yield with 95/5 b / l . The linear aliphatic alkynes ( 1t– 1v ) could be reacted to afford 3t – 3v in 72–82% yields with 88/12–98/2 b / l . Additionally, alkynes that incorporate bioactive molecules, such as sesamol ( 1w ), naproxen ( 1x ), ibuprofen ( 1 y ), and the fragment of empagliflozin ( 1z ), could be employed to deliver the corresponding products 3w - 3z in 46–71% yields with 88/12–96/4 b / l , further demonstrating the utilities of the approach.

a 1 (0.50 mmol), 2 (0.25 mmol), CoBr 2 (5 mol%), L1 (6 mol%), OMTS (1.2 eq.), LiOMe (2.5 eq.), THF (2.0 mL), 40 °C for 12 h, yields were determined by 1 H NMR using 1,1,2,2-tetrachloroethane or 1,3,5-trimethoxybenzene as an internal standard, isolated yields are shown in parentheses. b L1 •CoBr (5 mol%) instead of CoBr 2 and L1 . c At 45 °C. d At 35 °C.

Inspired by the effectiveness of thiol-ene and thiol-yne reactions in connecting two molecular structures through the robust creation of carbon-sulfur bonds 42 , 43 , 44 , 45 , 46 , 47 , the diverse synthesis based on this protocol has been conducted (Fig. 3 ). The sulfur source available for this protocol can be easily obtained from alcohols, halides, and disulfides. Aryl alkynes could undergo hydrothiolation with aliphatic sulfurizing reagents such as deuterated methyl ( 2ab ), ethyl ( 2ac ), cyclopropylmethyl ( 2ad ), butyl ( 2ae ), isopropyl ( 2af ), and trifluoropropyl ( 2ag ), obtaining the corresponding products ( 3ab – 3af ) in 28–84% yields with 90/10–97/3 b / l . Sulfurizing reagents bearing substituted benzyl groups ( 2ah, 2ai ), could also be transformed into the desired products ( 3ah, 3ai ). To our delight, the reaction between 1aj and 2aj resulted in 3aj in 86% yield and 92/8 b / l , proving the suitability of aryl alkynes with aryl sulfurizing reagents. The reaction between alkyl alkynes and alkyl or aryl sulfurizing reagents is also suitable to obtain the corresponding products ( 3ak – 3am ) in 35–81% yields with 86/14–93/7 b / l . Masked sulfurizing reagents derived from 1-adamantaneethanol ( 2an ), geraniol ( 2ao ), and naproxen ( 2ap ), successfully reacted with alkynes to obtain 3an – 3ap in 51–80% yields with 96/4– > 99/1 b / l , confirming the universality of diverse synthesis.

a 1 (0.50 mmol), 2 (0.25 mmol), CoBr 2 (5 mol%), L1 (6 mol%), OMTS (1.2 eq.), LiOMe (2.5 eq.), THF (2.0 mL), 40 °C for 12 h, yields were determined by 1 H NMR using 1,1,2,2-tetrachloroethane or 1,3,5-trimethoxybenzene as an internal standard, isolated yields are shown in parentheses. b L1 •CoBr (5 mol%) was used instead of CoBr 2 and L1 . c At 45 °C. d At room temperature. e 59 h. f Ph 2 SiH 2 instead of OMTS.

Gram-scale reaction and synthetic applications

The reaction could be smoothly conducted on a 10 mmol scale, yielding 1.34 grams of the methyl(1-phenylvinyl)sulfane 3aa with an impressive 89% yield (Fig. 4a ), facilitating subsequent derivatization with ease. The Ts- group utilzied in the reaction can be efficiently recovered in the form of lithium 4-methylbenzenesulfinate ( 5 ) and regenerated into the starting material ( 2a ) through a two-step conversion. This capability showcases the remarkable recyclability and sustainability of the employed protocol. To showcase the utility of the branched alkenyl sulfides, further transformations of 3aa were investigated (Fig. 4b ). The product 3aa could undergo nickel-catalyzed Kumada coupling with methylmagnesium bromide to obtain 1,1-dialkene ( 6 ) in 80% yield 18 , 48 . With copper catalysis, 3aa could proceed hydroboronation to deliver compound 7 in 95% yield 49 , 50 . The double bond of 3aa could be reduced with p -toluenesulfonyl hydrazide to obtain α -methylthioethylbenzene ( 8 ) in 90% yield 51 . The [2 + 1] cycloaddition reaction of difluorocarbene to 3aa could give difluorocyclopropane ( 9 ) in 87% yield 52 . Additionally, given that selenium belongs to the same chalcogen family as sulfur and has a similar electronegativity, Markovnikov hydroselenation of alkynes was likewise found to be a viable reaction pathway (Fig. 4c ) 53 .

a Gram-scale reaction and recycling of byproduct. b Synthetic applications of the product. c Hydroselenation of alkynes. d Aerobic oxidation of the product in air.

Unlike linear alkenyl sulfides, branched alkenyl sulfides are highly sensitive to air (Fig. 4d ). Exemplified by 3aa , it could readily undergo oxidation in the presence of air, transforming into 2-(methylthio)-1-phenylethan-1-one ( 10 ). This reactivity explains why 3aa can be easily stained with (2,4-dinitrophenyl)hydrazine on thin-layer chromatography plates. To our knowledge, this phenomenon has never been reported. However, upon scrutinizing the supporting information from prior works on the synthesis of branched alkenyl sulfides, traces of oxidized products can be observed in the 1 H NMR spectra. A possible mechanism involving radical addition has been proposed (see SI). The sensitivity of branched alkenyl sulfides to oxygen provides insights into the potential applications in ROS probes 54 . It should be emphasized that branched alkenyl sulfides are quite stable in a nitrogen-filled glove box, with no oxidation products observed after six months of storage.

Mechanistic investigation

To elucidate the reaction mechanism, a range of comparative experiments were undertaken. The addition of 2.0 equivalents of radical scavengers to the reaction system did not obstruct the course of the reaction (Fig. 5a ). Introduction of deuterated phenylacetylene or deuterated diphenylsilane into the reaction elucidated the emergence of two identical deuterated ratio at the β -position of the product (Fig. 5b ). To excluded the possibility of the possibility of the process that β -H elimination followed by branched alkenyl methylsulfanes inserting into Co–H pathway, 3aa was added to the reaction with deuterated diphenylsilane, and no significant deuterium substitution was observed in the recovered 3aa . To ruled out the possible H-D exchange between terminal alkynes and silanes, 1,2-diphenylacetylene was used as a raw material for the reaction, resulting in 11 in equivalent yield with E / Z = 1/4 (Fig. 5c ). The observations suggest the conceivable occurrence of a Crabtree-Ojima isomerization process. When L1 •CoOMe was used as catalyst, the targeted product was obtained in 84% yield. When lithium methoxide was removed, only trace of product was obtained (Fig. 5d ). This means that in this system, despite being classified as a cobalt oxide species, the coordination between the toluenesulfonate anion and the central metal was relatively robust, making it impossible to regenerate cobalt hydride species from silane. The reaction exhibited an induction period before reaching maximum yield approximately after eight hours (see SI). An in-depth analysis of the influence of different substituents on phenylacetylene on the reaction kinetics facilitatedthe construction of a Hammett plot (Supplementary Data 1 ), which unveiled a positive slope. The observation implies that the turnover-limiting step is dominated by negative charge accumulation, providing valuable insights into the reaction mechanism (Fig. 5e ).

a Radical trapping experiments. b Deuterium-labeling experiments. c Hydromethylthiolation of the internal alkyne. d Control experiments. e Hammett plot.

Based on these mechanistic experiments and previous literatures 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , a possible reaction mechanism is proposed (Fig. 6 ). The initial steps involve cobalt bromide species undergoing ligand exchange with lithium methoxide to obtain cobalt hydride species II upon interaction with silane. Subsequent alkyne coordination is followed by cobalt hydride insertion, resulting in the formation of an alkenyl cobalt intermediate IV . This species further undergoes reaction with the masked sulfurizing reagent, proceeding through a five-membered ring pathway. Ultimately, the branched alkenyl sulfide and cobalt p -toluenesulfonate V are obtained 65 . Then V undergoes ligand exchange with lithium methoxide, initiating the subsequent catalytic cycle.

Proposed reaction pathway starts from Co–OMe, then alkyne inserts into Co–H, followed by the reaction with the masked sulfurizing reagent.

In summary, a cobalt-catalyzed hydromethylthiolation reaction of alkynes have been developed, which also enables the diverse synthesis of branched alkenyl sulfides and exhibits remarkable regioselectivity and compatibility within a wide range of substrates, including alkenes, esters, amines, and aryl halides. This protocol provides an efficient route obtaining a series of alkenyl sulfides from readily available starting materials. Preliminary investigations into the reaction mechanism provide evidence for alkyne insertion into cobalt hydrides, followed by reaction with the masked sulfurizing reagents. The studies on aerobic oxidation of alkenyl sulfides revealed blind spots in literatures. Further studies on the synthesis and applications of alkenyl sulfides are undergoing.

General procedure for the synthesis of branched alkenyl sulfides

In a nitrogen-filled glove box, a 10 mL vial equipped with a stir bar was charged with CoBr 2 (0.0125 mmol, 5 mol%), L1 (0.015 mmol, 6 mol%), LiOMe (0.625 mmol, 2.5 eq.), and THF (2.0 mL). The mixture was stirred for about 20 min to afford a golden solution. Then OMTS (1,1,1,3,5,7,7,7-octamethyltetrasiloxane, 1.2 eq., ρ = 0.863 g/mL), alkyne (0.50 mmol, 2.0 eq.), and TsSR 2 (0.25 mmol, 1.0 eq.) were added sequentially (dropwise if liquid). The vial was sealed, removed from the glove box, and stirred at 40 °C for 12 h. The reaction mixture was quenched by PE (20 mL), filtered through a short pad of silica, and eluted with ether or PE/EA (5/1). The combined filtrate was concentrated under reduced pressure at 40 °C and flushed with nitrogen gas, then purified by flash column chromatography to give the corresponding product (Caution: the flask must be flushed with nitrogen gas after concentration steps due to the instability of the product under the air).

Data availability

The authors declare that the data Supplementary the findings of this study are available within the paper and its Supplementary Information file. The experimental procedures and characterization of all new compounds are provided in the Supplementary Information. Data supporting the findings of this manuscript are also available from the authors upon request.

Tewari, N. et al. An improved procedure for preparation of carbapenem antibiotic: Meropenem. Org. Process Res. Dev. 11 , 773–775 (2007).

Article Google Scholar

Greger, H., Zechner, G., Hofer, O., Hadacek, F. & Wurz, G. Sulphur-containing amides from Glycosmis species with different antifungal activity. Phytochemistry 34 , 175–179 (1993).

Nakabayashi, K., Abiko, Y. & Mori, H. RAFT polymerization of S-vinyl sulfide derivatives and synthesis of block copolymers having two distinct optoelectronic functionalities. Macromolecules 46 , 5998–6012 (2013).

Article ADS Google Scholar

Wang, N., Saidhareddy, P. & Jiang, X. Construction of sulfur-containing moieties in the total synthesis of natural products. Nat. Prod. Rep. 37 , 246–275 (2020).

Article PubMed Google Scholar

Velasco, N., Virumbrales, C., Sanz, R., Suarez-Pantiga, S. & Fernandez-Rodriguez, M. A. General synthesis of alkenyl sulfides by palladium-catalyzed thioetherification of alkenyl halides and tosylates. Org. Lett. 20 , 2848–2852 (2018).

Beletskaya, I. P. & Ananikov, V. P. Transition-metal-catalyzed C−S, C−Se, and C−Te bond formation via cross-coupling and atom-economic addition reactions. Chem. Rev. 111 , 1596–1636 (2011).

Wang, Z. et al. Research progress in C—S bond formation reaction of olefins with organic sulfur reagents under photocatalyst-free and non-electrochemical conditions. Chin. J. Org. Chem. 41 , 171–184 (2021).

Beletskaya, I. P. & Ananikov, V. P. Transition-Metal-Catalyzed C-S, C-Se, and C-Te bond formations via cross-coupling and atom-economic addition reactions. achievements and challenges. Chem. Rev. 122 , 16110–16293 (2022).

Castarlenas, R., Di Giuseppe, A., Perez-Torrente, J. J. & Oro, L. A. The emergence of transition-metal-mediated hydrothiolation of unsaturated carbon-carbon bonds: a mechanistic outlook. Angew. Chem. Int. Ed. 52 , 211–222 (2013).

Chen, J., Wei, W.-T., Li, Z. & Lu, Z. Metal-catalyzed Markovnikov-type selective hydrofunctionalization of terminal alkynes. Chem. Soc. Rev. 53 , 7566–7589 (2024).

Ogawa, A. Activation and reactivity of Group 16 inter-element linkage—transition-metal-catalyzed reactions of thiols and selenols. J. Organomet. Chem. 611 , 463–474 (2000).

Ogawa, A., Ikeda, T., Kimura, K. & Hirao, T. Highly regio- and stereocontrolled synthesis of vinyl sulfides via transition-metal-catalyzed hydrothiolation of alkynes with thiols. J. Am. Chem. Soc. 121 , 5108–5114 (1999).

Kuniyasu, H. et al. The first example of transition-metal-catalyzed addition of aromatic thiols to acetylenes. J. Am. Chem. Soc. 114 , 5902–5903 (1992).

Degtyareva, E. S. et al. Pd-NHC catalytic system for the efficient atom-economic synthesis of vinyl sulfides from tertiary, secondary, or primary thiols. ACS Catal. 5 , 7208–7213 (2015).

Ananikov, V. P. et al. New approach for size- and shape-controlled preparation of Pd nanoparticles with organic ligands. Synthesis and Application in Catalysis. J. Am. Chem. Soc. 129 , 7252–7253 (2007).

Di Giuseppe, A. et al. Ligand-controlled regioselectivity in the hydrothiolation of alkynes by rhodium N-heterocyclic carbene catalysts. J. Am. Chem. Soc. 134 , 8171–8183 (2012).

Yang, J., Sabarre, A., Fraser, L. R., Patrick, B. O. & Love, J. A. Synthesis of 1,1-disubstituted alkyl vinyl sulfides via rhodium-catalyzed alkyne hydrothiolation: Scope and limitations. J. Org. Chem. 74 , 182–187 (2009).

Sabarre, A. & Love, J. Synthesis of 1,1-disubstituted olefins via catalytic alkyne hydrothiolation/Kumada cross-coupling. Org. Lett. 10 , 3941–3944 (2008).

Palacios, L. et al. Hydroxo–rhodium–N-heterocyclic carbene complexes as efficient catalyst precursors for alkyne hydrothiolation. ACS Catal. 3 , 2910–2919 (2013).

Cao, C., Fraser, L. R. & Love, J. A. Rhodium-catalyzed alkyne hydrothiolation with aromatic and aliphatic thiols. J. Am. Chem. Soc. 127 , 17614–17615 (2005).

Weiss, C. J., Wobser, S. D. & Marks, T. J. Organoactinide-mediated hydrothiolation of terminal alkynes with aliphatic, aromatic, and benzylic thiols. J. Am. Chem. Soc. 131 , 2062–2063 (2009).

Weiss, C. J., Wobser, S. D. & Marks, T. J. Lanthanide- and actinide-mediated terminal alkyne hydrothiolation for the catalytic synthesis of markovnikov vinyl sulfides. Organometallics 29 , 6308–6320 (2010).

Weiss, C. J. & Marks, T. J. Organozirconium complexes as catalysts for markovnikov-selective intermolecular hydrothiolation of terminal alkynes: Scope and mechanism. J. Am. Chem. Soc. 132 , 10533–10546 (2010).

Zhang, Y., Xu, X. & Zhu, S. Nickel-catalysed selective migratory hydrothiolation of alkenes and alkynes with thiols. Nat. Commun. 10 , 1752–1762 (2019).

Article ADS PubMed PubMed Central Google Scholar

Malyshev, D. A. et al. Homogeneous nickel catalysts for the selective transfer of a single arylthio group in the catalytic hydrothiolation of alkynes. Organometallics 25 , 4462–4470 (2006).

Han, L., Zhang, C., Yazawa, H. & Shimada, S. Efficient and selective Nickel-catalyzed addition of H−P(O) and H−S bonds to alkynes. J. Am. Chem. Soc. 126 , 5080–5081 (2004).

Ananikov, V. P., Orlov, N. V. & Beletskaya, I. P. Efficient and convenient synthesis of β-vinyl sulfides in nickel-catalyzed regioselective addition of thiols to terminal alkynes under solvent-free conditions. Organometallics 25 , 1970–1977 (2006).

Burykina, J. V., Shlapakov, N. S., Gordeev, E. G., Konig, B. & Ananikov, V. P. Selectivity control in thiol-yne click reactions via visible light induced associative electron upconversion. Chem. Sci. 11 , 10061–10070 (2020).

Article PubMed PubMed Central Google Scholar

Beno, B. R., Yeung, K. S., Bartberger, M. D., Pennington, L. D. & Meanwell, N. A. A survey of the role of noncovalent sulfur interactions in drug design. J. Med. Chem. 58 , 4383–4438 (2015).

Barreiro, E. J., Kümmerle, A. E. & Fraga, C. A. M. The methylation effect in medicinal chemistry. Chem. Rev. 111 , 5215–5246 (2011).

Teders, M. et al. The energy-transfer-enabled biocompatible disulfide-ene reaction. Nat. Chem. 10 , 981–988 (2018).

Kokin, K., Tsuboi, S., Motoyoshiya, J. & Hayashi, S. Selective synthesis of cis - α , β -unsaturated sulfoxides and sulfides by the horner-wittig reaction with bis(2,2,2-trifluoroethyl)phosphono sulfoxides and aromatic aldehydes. Synthesis 1996 , 637–640 (1996).

Barre, V. & Uguen, D. Cyclopentanation with β-methylthio-allyl phenyl sulfone. Tetrahedron Lett. 28 , 6045–6048 (1987).

Wang, M., Qiao, Z., Zhao, J. & Jiang, X. Palladium-catalyzed thiomethylation via a three-component cross-coupling strategy. Org. Lett. 20 , 6193–6197 (2018).

Qiao, Z., Ge, N. & Jiang, X. CO 2 -promoted oxidative cross-coupling reaction for C-S bond formation via masked strategy in an odourless way. Chem. Commun. 51 , 10295–10298 (2015).

Xiao, X., Feng, M. & Jiang, X. New design of a disulfurating reagent: Facile and straightforward pathway to unsymmetrical disulfanes by copper-catalyzed oxidative cross-coupling. Angew. Chem. Int. Ed. 55 , 14121–14125 (2016).

Qiao, Z. & Jiang, X. Recent developments in sulfur–carbon bond formation reaction involving thiosulfates. Org. Biomol. Chem. 15 , 1942–1946 (2017).

Wei, Y., Gao, W., Chang, H. & Jiang, X. Recent advances in thiolation via sulfur electrophiles. Org. Chem. Front. 9 , 6684–6707 (2022).

Zhang, C. et al. Cesium carbonate-promoted synthesis of aryl methyl sulfides using S -methylisothiourea sulfate under transition-metal-free conditions. Org. Biomol. Chem. 16 , 6316–6321 (2018).

Article ADS PubMed Google Scholar

Martín, A. J., Mitchell, S., Mondelli, C., Jaydev, S. & Pérez-Ramírez, J. Unifying views on catalyst deactivation. Nat. Catal. 5 , 854–866 (2022).

Aubart, M. A. & Bergman, R. G. Activation of organic disulfides by a paramagnetic heterobimetallic tantalum/cobalt complex and a comparison of their reactions with cobaltocene. Evidence for a dependence of mechanism on the electronic properties of the disulfide. J. Am. Chem. Soc. 118 , 1793–1794 (1996).

Cao, T., Xu, T., Xu, R., Shu, X. & Liao, S. Decarboxylative thiolation of redox-active esters to free thiols and further diversification. Nat. Commun. 11 , 5340 (2020).

Dénès, F., Pichowicz, M., Povie, G. & Renaud, P. Thiyl radicals in organic synthesis. Chem. Rev. 114 , 2587–2693, (2014).

Boutureira, O. & Bernardes, G. J. L. Advances in chemical protein modification. Chem. Rev. 115 , 2174–2195 (2015).

Boyd, D. A. Sulfur and its role in modern materials science. Angew. Chem. Int. Ed. 55 , 15486–15502 (2016).

Kumar, R. et al. Thiol-ene “click” reaction triggered by neutral ionic liquid: the “ambiphilic” character of [hmim]Br in the regioselective nucleophilic hydrothiolation. Angew. Chem. Int. Ed. 54 , 828–832 (2015).

Xiang, S., Ding, W., Wang, Y. & Tan, B. Catalytic atroposelective synthesis. Nat. Catal. 7 , 483–498 (2024).

Huang, S., Wang, M. & Jiang, X. Ni-catalyzed C-S bond construction and cleavage. Chem. Soc. Rev. 51 , 8351–8377 (2022).

Corberán, R., Mszar, N. W. & Hoveyda, A. H. NHC-Cu-catalyzed enantioselective hydroboration of acyclic and exocyclic 1,1-disubstituted aryl alkenes. Angew. Chem. Int. Ed. 50 , 7079–7082 (2011).

Laitar, D. S., Müller, P. & Sadighi, J. P. Efficient homogeneous catalysis in the reduction of CO 2 to CO. J. Am. Chem. Soc. 127 , 17196–17197 (2005).

Cloarec, J.-M. & Charette, A. B. Highly efficient two-step synthesis of C-sp 3 -centered geminal diiodides. Org. Lett. 6 , 4731–4734 (2004).

Wang, F. et al. Synthesis of gem-difluorinated cyclopropanes and cyclopropenes: Trifluoromethyltrimethylsilane as a difluorocarbene source. Angew. Chem. Int. Ed. 50 , 7153–7157 (2011).

Slocumb, H. S., Nie, S., Dong, V. M. & Yang, X.-H. Enantioselective selenol-ene using Rh-hydride catalysis. J. Am. Chem. Soc. 144 , 18246–18250 (2022).

Lou, Z., Li, P. & Han, K. Redox-responsive fluorescent probes with different design strategies. Acc. Chem. Res. 48 , 1358–1368 (2015).

Chen, J., Shen, X. & Lu, Z. Cobalt-catalyzed markovnikov-type selective hydroboration of terminal alkynes. Angew. Chem. Int. Ed. 60 , 690–694 (2021).

Chen, J., Ying, J. & Lu, Z. Cobalt-catalyzed branched selective hydroallylation of terminal alkynes. Nat. Commun. 13 , 4518–4528 (2022).

Ojima, I., Clos, N., Donovan, R. J. & Ingallina, P. Hydrosilylation of 1-hexyne catalyzed by rhodium and cobalt-rhodium mixed-metal complexes. Mechanism of apparent trans addition. Organometallics 9 , 3127–3133 (1990).

Zhang, R. et al. Thiyl radical trapped by cobalt catalysis: An approach to markovnikov thiol–ene reaction. Org. Lett. 26 , 591–596 (2024).

Zhang, Y., Hu, L., Yuwen, L., Lu, G. & Zhang, Q. Nickel-catalysed enantioselective hydrosulfenation of alkynes. Nat. Catal. 6 , 487–494 (2023).

Li, Y., Lu, X. & Fu, Y. Recent advances in cobalt-catalyzed regio- or stereoselective hydrofunctionalization of alkenes and alkynes. CCS Chem. 0 , 1–27 (2024).

Google Scholar

Ai, W., Zhong, R., Liu, X. & Liu, Q. Hydride transfer reactions catalyzed by cobalt complexes. Chem. Rev. 119 , 2876–2953 (2019).

Chen, J., Guo, J. & Lu, Z. Recent advances in hydrometallation of alkenes and alkynes via the first row transition metal catalysis. Chin. J. Chem. 36 , 1075–1109 (2018).

Liu, R. Y. & Buchwald, S. L. CuH-catalyzed olefin functionalization: From hydroamination to carbonyl addition. Acc. Chem. Res. 53 , 1229–1243 (2020).

Uehling, M. R., Rucker, R. P. & Lalic, G. Catalytic anti-markovnikov hydrobromination of alkynes. J. Am. Chem. Soc. 136 , 8799–8803 (2014).

Fang, Y., Rogge, T., Ackermann, L., Wang, S. & Ji, S. Nickel-catalyzed reductive thiolation and selenylation of unactivated alkyl bromides. Nat. Commun. 9 , 2240–2249 (2018).

Download references

Acknowledgements

Financial supports were provided by the National Key R&D Program of China (2021YFF0701600 and 2021YFA1500200), the NSFC (22271249), the Fundamental Research Funds for the Central Universities (226-2022-00224 and 226-2024-00003), and Zhejiang Provincial Natural Science Foundation of China (LDQ24B020001). Our sincere thanks to Dr. Jieping Chen for providing the initial results, Mr. Chengong Zheng for his attempts to single crystal growth, and Mr. Lingtao Wang for polishing the manuscript.

Author information

Authors and affiliations.

Center of chemistry for Frontier Technologies, Department of Chemistry, Zhejiang University, Hangzhou, 310058, China

Jiale Ying & Zhan Lu

College of Chemistry, Zhengzhou University, Zhengzhou, 450001, China

Yan Tan & Zhan Lu

You can also search for this author in PubMed Google Scholar

Contributions

Z.L. proposed this project. J.Y. and Y.T. performed the experiments. Z.L. and J.Y. prepared the manuscript. J.Y. and Y.T. prepared the Supplementary Information.

Corresponding author

Correspondence to Zhan Lu .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Communications thanks Lan-Gui Xie, and the other, anonymous, reviewers for their contribution to the peer review of this work. A peer review file is available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information, peer review file, description of additional supplementary files, supplementary data 1, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Ying, J., Tan, Y. & Lu, Z. Cobalt-catalyzed hydrothiolation of alkynes for the diverse synthesis of branched alkenyl sulfides. Nat Commun 15 , 8057 (2024). https://doi.org/10.1038/s41467-024-52249-x

Download citation

Received : 30 May 2024

Accepted : 28 August 2024

Published : 14 September 2024

DOI : https://doi.org/10.1038/s41467-024-52249-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

Explore articles by subject
Guide to authors
Editorial policies

Management of Rotylenchulus reniformis in soybean using genetic and biological approaches

Original Article
Published: 09 September 2024

Cite this article

Rafaela Bueno Loreto 1 ,
Santino Aleandro da Silva 2 &
Andressa Cristina Zamboni Machado ORCID: orcid.org/0000-0003-4383-2221 1 , 2

10 Accesses

Explore all metrics

Rotylenchulus reniformis poses a significant threat to cotton crops in the Brazilian Cerrado, particularly when grown consecutively with soybeans. This nematode has not only become a concern for cotton but has also led to considerable damage in soybean crops, emphasizing the need for effective nematode control in both agricultural settings. The aim of this study was to combine genetic control with the application of biological nematicides, as seed treatment, to manage R. reniformis under greenhouse conditions. Two soybean cultivars, TMG 4182 and Fibra, resistant and susceptible, were used and the biological nematicides used included Purpureocillium lilacinum, Trichoderma harzianum + T. asperellum + Bacillus amyloliquefaciens , B. subtilis + B. licheniformis , and B. firmus . Inoculation with 800 R. reniformis occurred in the cotyledonary stage, with evaluations conducted at 72 and 76 days after inoculation for Experiments 1 and 2, respectively. Nematodes were extracted from the soil and roots, calculating the reproduction factor (RF). The combination of biological nematicides with resistant cultivars did not yield substantial benefits in controlling reniform nematodes in soybean but safeguarding resistant cultivars through the application of chemical or biological nematicides is important to mitigate inoculum pressure on resistance genes. In addition, biological nematicides evaluated in this study did not improve soybean plant development and we concluded that managing reniform nematodes in soybean necessitates the integration of diverse control measures to effectively address the challenges posed by this nematode's impact on crops.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

Get 10 units per month
Download Article/Chapter or eBook
1 Unit = 1 Article or 1 Chapter
Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Seed treatments associated with resistance inducers for management of Pratylenchus brachyurus in soybean

Experimental and statistical approaches to evaluate the reaction of soybean to Pratylenchus brachyurus

Reaction of soybean genotypes to the nematodes Meloidogyne incognita and M. javanica

Availability of data and material.

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

Not applicable.

Agrofit (2022) Produtos técnicos registrados pelo Ministério da Agricultura, Pecuária e Abastecimento do Brasil. Consulta pública online. Available at: http://agrofit.agricultura.gov.br/agrofit_cons/principal_agrofit_cons . Accessed on June 24, 2022

Boneti JIS, Ferraz S (1981) Modificação do método de Hussey & Barker para extração de ovos de Meloidogyne exigua de cafeeiro. Fitopatol Bras 6:553

Google Scholar

Castillo JD, Lawrence KS, Kloepper JW, Van Santen E (2010) Evaluation of Dreschlerella dactyloides, Dreschlerella brochopaga, and Paecilomyces lilacinus for the biocontrol of Rotylenchulus reniformis . Nematropica 40:71–85

Dallemole-giaretta R, Freitas LG, Lopes EA, Pereira OI, Zooca RJF, Ferraz S (2012) Screening of Pochonia chlamydosporia Brazilian isolates as biocontrol agents of Meloidogyne javanica . J Crop Protect 42:102–107

Article Google Scholar

Dias-Arieira CR, Santana-Gomes SM, Miamoto A, Machado ACZ (2022) Manejo Biológico de Nematoides. In: Meyer MC, Bueno AF, Mazaro SM, Silva JC (eds) Bioinsumos na cultura da soja. Embrapa Soja, Londrina, pp 345–358

Dias-Arieira CR, Miamoto A, Machado ACZ (2023) Manejo integrado de nematoides. Dias-Arieira CR, Araújo FG, Machado ACZ (Org) Manejo de nematoides em grandes culturas. NPCT, Piracicaba, pp 209–217

Favoreto L, Meyer MC, Dias-Arieira CR, Machado ACZ, Santiago DC, Ribeiro NR (2019) Diagnose e manejo de fitonematoides na cultura da soja, Belo Horizonte, MG. Informe Agropecuário 40:18–29

Lobo KSRS, Vieira SP, Moraes RA, Silva MR (2015) Avaliação da reação de genótipos de soja a Rotylenchulus reniformis . Anais XXXII Congresso Brasileiro de Nematologia. SBN, Londrina, p 119

Machado ACZ (2022) Bionematicides in Brazil: an emerging and challenging market. Revisão Anual de Patologia de Plantas 28:35–49

Machado ACZ, Silva SA (2019) Extração de nematoides. In: Machado ACZ, Silva SA, Ferraz LCCB (eds) Métodos em Nematologia Agrícola. Filipel Gráfica e Editora, Sociedade Brasileira de Nematologia, Piracicaba, pp 9–12

R Core Team (2020) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna Available at: https://www.R-project.org/ . Accessed on June 20, 2022

Robinson AF, Inserra RN, Caswell-Chen EP, Vovlas N, Troccoli A (1997) Rotylenchulus species: identification, distribution, host ranges, and crop plant resistance. Nematropica 27:127–180

Silva RA, Machado ACZ, Santos TFS, Silva RG (2019) Nematoides no sistema de produção. Boletim de Pesquisa Fundação MT, Rondonópolis, pp 191–210

Silva RA, Santos TFS, Asmus GL (2021) Reprodução do nematoide reniforme em cultivares de soja que antecedem o algodoeiro no cerrado. Nematropica 51:137–144

Silva RA, Machado ACZ, Santos TFS, Silva RG, Oliveira JN (2023) Nematoides nos sistemas de produção agrícola. Boletim de Pesquisa Fundação MT, Rondonópolis, pp 179–190

Wang K, Riggs RD, Crippen D (2005) Isolation, selection, and efficacy of Pochonia chlamydosporia for control of Rotylenchulus reniformis on cotton. Phytopathology 95:890–893

Article PubMed Google Scholar

Yang F, Abdelnabby H, Xiao Y (2015) The role of a phospholipase (PLD) in virulence of Purpureocillium lilacinum ( Paecilomyces lilacinus ). Microb Pathogen 85:11–20

Article CAS Google Scholar

Download references

Acknowledgments

A. C. Z. Machado would like to thank Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq, for the grants awarded.

Author information

Authors and affiliations.

Universidade Estadual de Londrina, Londrina, Paraná, Brasil

Rafaela Bueno Loreto & Andressa Cristina Zamboni Machado

Agronema Análise, Consultoria e Experimentação Nematológicas, Londrina, Paraná, Brasil

Santino Aleandro da Silva & Andressa Cristina Zamboni Machado

You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Rafaela Bueno, and Santino A. Silva. The first draft of the manuscript was written by Andressa C. Z. Machado, Santino Silva and Rafaela Bueno and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Andressa Cristina Zamboni Machado .

Ethics declarations

Conflicts of interest, disclosure of potential conflicts of interest, research involving human participants and/or animals, informed consent, additional information, publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Loreto, R.B., da Silva, S.A. & Machado, A.C.Z. Management of Rotylenchulus reniformis in soybean using genetic and biological approaches. Trop. plant pathol. (2024). https://doi.org/10.1007/s40858-024-00687-9

Download citation

Received : 27 February 2024

Accepted : 19 August 2024

Published : 09 September 2024

DOI : https://doi.org/10.1007/s40858-024-00687-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Bacillus spp.
Glycine max
Reniform nematode
Purpureocillium lilacinum
Trichoderma sp
Find a journal
Publish with us
Track your research

IMAGES

Controlled Experiment
What An Experimental Control Is And Why It’s So Important
What Is a Controlled Experiment?
What Best Describes a Controlled Experiment
PPT
Experiment. Independent, dependent, key variables and control (Ms

VIDEO

Control servo motor with arduino. Experiment lab
Esc201a Lab Experiment 3.flv
PID Control Lab
Counting Cells: Control the epidemic
PID Control Lab
Control Lab

COMMENTS

What Is a Controlled Experiment?
Revised on June 22, 2023. In experiments, researchers manipulate independent variables to test their effects on dependent variables. In a controlled experiment, all variables other than the independent variable are controlled or held constant so they don't influence the dependent variable. Controlling variables can involve:
What An Experimental Control Is And Why It's So Important
An experimental control is used in scientific experiments to minimize the effect of variables which are not the interest of the study. The control can be an object, population, or any other variable which a scientist would like to "control." ... A control is important for an experiment because it allows the experiment to minimize the changes in ...
What Is a Controlled Experiment?
In experiments scientists compare a control group and an experimental group that are identical in all respects, except for one difference - experimental manipulation. ... In an experiment, the control is a standard or baseline group not exposed to the experimental treatment or manipulation. It serves as a comparison group to the experimental ...
Controlled experiments (article)
Controlled experiments (article) | Khan Academy. If you're seeing this message, it means we're having trouble loading external resources on our website. If you're behind a web filter, please make sure that the domains *.kastatic.org and *.kasandbox.org are unblocked. Khanmigo is now free for all US educators! Plan lessons, develop exit tickets ...
Controlled Experiments
Control in experiments is critical for internal validity, which allows you to establish a cause-and-effect relationship between variables. Example: Experiment. You're studying the effects of colours in advertising. You want to test whether using green for advertising fast food chains increases the value of their products.
Controlled Experiments: Definition and Examples
A controlled experiment is a research study in which participants are randomly assigned to experimental and control groups. A controlled experiment allows researchers to determine cause and effect between variables. One drawback of controlled experiments is that they lack external validity (which means their results may not generalize to real ...
Controlled Experiments: Definition, Steps, Results, Uses
Controlled Experiments: Definition, Steps, Results, Uses. Controlled experiments ensure valid and reliable results by minimizing biases and controlling variables effectively. Rigorous planning, ethical considerations, and precise data analysis are vital for successful experiment execution and meaningful conclusions.
Control Groups and Treatment Groups
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).
Why control an experiment?
P < 0.05 tacitly acknowledges the explicate order. Another example of the "subjectivity" of our perception is the level of accuracy we accept for differences between groups. For example, when we use statistical methods to determine if an observed difference between control and experimental groups is a random occurrence or a specific effect, we conventionally consider a p value of less than ...
Controlled Experiment
Controlled Experiment Definition. A controlled experiment is a scientific test that is directly manipulated by a scientist, in order to test a single variable at a time. The variable being tested is the independent variable, and is adjusted to see the effects on the system being studied. The controlled variables are held constant to minimize or ...
Scientific control
A scientific control is an experiment or observation designed to minimize the effects of variables other than the independent variable (i.e. confounding variables). [1] ... For example, in experiments where crop yield is affected (e.g. soil fertility), the experiment can be controlled by assigning the treatments to randomly selected plots of ...
What Is a Controlled Experiment?
Controlled Experiment. A controlled experiment is simply an experiment in which all factors are held constant except for one: the independent variable. A common type of controlled experiment compares a control group against an experimental group. All variables are identical between the two groups except for the factor being tested.
Control Variables
Control variables in experiments. In an experiment, a researcher is interested in understanding the effect of an independent variable on a dependent variable. Control variables help you ensure that your results are solely caused by your experimental manipulation. Example: Experiment You want to study
What Is a Control Variable? Definition and Examples
A single experiment may contain many control variables. Unlike the independent and dependent variables, control variables aren't a part of the experiment, but they are important because they could affect the outcome. Take a look at the difference between a control variable and control group and see examples of control variables.
Experimental Method In Psychology
There are three types of experiments you need to know: 1. Lab Experiment. A laboratory experiment in psychology is a research method in which the experimenter manipulates one or more independent variables and measures the effects on the dependent variable under controlled conditions. A laboratory experiment is conducted under highly controlled ...
What Is a Control in an Experiment? (Definition and Guide)
When conducting an experiment, a control is an element that remains unchanged or unaffected by other variables. It's used as a benchmark or a point of comparison against which other test results are measured. Controls are typically used in science experiments, business research, cosmetic testing and medication testing.
Controlled Experiment
The steps are: Ask a question. Research the topic. Develop a hypothesis. Test with an experiment. Analyze data. Report conclusions. Developing a controlled experiment is one way to test a ...
Control Group Definition and Examples
For example, consider an experiment looking at whether a new drug kills bacteria. The experimental group exposes bacterial cultures to the drug. If the group survives, the drug is ineffective. If the group dies, the drug is effective. The positive control group has a culture of bacteria that carry a drug resistance gene.
Controlled Experiments: Methods, Examples & Limitations
Research. Controlled Experiments: Methods, Examples & Limitations. What happens in experimental research is that the researcher alters the independent variables so as to determine their impacts on the dependent variables. Therefore, when the experiment is controlled, you can expect that the researcher will control all other variables except for ...
Positive Control vs Negative Control: Differences & Examples
A positive control is designed to confirm a known response in an experimental design, while a negative control ensures there's no effect, serving as a baseline for comparison.. The two terms are defined as below: Positive control refers to a group in an experiment that receives a procedure or treatment known to produce a positive result. It serves the purpose of affirming the experiment's ...
7 Types of Experiment Controls
Double-Blind Experiment. The practice of not informing some of the experimenters which of the participants in an experiment are in a control group. This is done to eliminate unintentional bias. For practical reasons, some of the experimenters may need to know the members of the control group such as the individual who performs the randomization.
Biology: Controlled Experiments
This animation describes the process of conducting a controlled experiment. This is the first video in a two-part series. Watch the second video, "Independen...
Control Group Vs Experimental Group In Science
In a controlled experiment, scientists compare a control group, and an experimental group is identical in all respects except for one difference - experimental manipulation.. Differences. Unlike the experimental group, the control group is not exposed to the independent variable under investigation. So, it provides a baseline against which any changes in the experimental group can be compared.
1.4 Designed Experiments
The control group helps researchers balance the effects of being in an experiment with the effects of the active treatments. Of course, if you are participating in a study and you know that you are receiving a pill that contains no actual medication, then the power of suggestion is no longer a factor.
What's unique about the CDAO's upcoming Global Information Dominance
The Pentagon's next Global Information Dominance Experiment — GIDE 12 — will put the minimum viable capability for Combined Joint All-Domain Command and Control that officials have been collaboratively refining in recent years via this rapid experimentation series, to its most international test yet.
Cobalt-catalyzed hydrothiolation of alkynes for the diverse ...
d Control experiments. e Hammett plot. Full size image. Based on these mechanistic experiments and previous literatures 55,56,57,58,59,60,61,62,63,64, a possible reaction mechanism is proposed ...
Management of Rotylenchulus reniformis in soybean using genetic and
For the susceptible cultivar in Experiment 1, no significant differences were noted between the nematicides and the inoculated control concerning the RF values of R. reniformis.However, P. lilacinum exhibited lower RF values compared to the treatment with B. subtilis + B licheniformis (Fig. 1).In Experiment 2, the RF values in the treatments with T. harzianum + T. asperellum + B ...

What An Experimental Control Is And Why It’s So Important

Daniel Nelson

Terminology Of A Scientific Experiment

Independent and Dependent Variables

Control Groups and Experimental Groups

Why Are Experimental Controls So Important?

A Practical Example

Not All Experiments Are Controlled

Related Posts

Have a language expert improve your writing

Controlled Experiments | Methods & Examples of Control

Table of contents

Prevent plagiarism, run a free check.

Control groups

Random assignment

Masking (blinding)

Difficult to control all variables

Risk of low external validity

Cite this Scribbr article

Is this article helpful?

Pritha Bhandari

Controlled Experiments: Definition, Steps, Results, Uses

Importance of controlled experiments in various fields

Why Controlled Experiments Matter?

Minimizing Biases and Confounding Variables

Achieving Causal Inference

Planning a Controlled Experiment

Identifying Variables and Control Groups

Designing Experimental Procedures and Protocols

Conducting a Controlled Experiment

Data Collection Methods and Instruments

Monitoring and Maintaining Experimental Conditions

Analysing Results and Drawing Conclusions

Interpretation of Results

Implications and Potential Applications

Common Challenges and Solutions

Dealing with Sample Size and Statistical Power

Mitigating Unforeseen Variables

Leave a Comment Cancel reply

Why control an experiment?

František Baluška

P < 0.05 tacitly acknowledges the explicate order

Further reading

Acknowledgements

Contributor Information

Controlled Experiment

Controlled Experiment Definition

Examples of Controlled Experiment

Scurvy in Sailors

Related Biology Terms

Cite This Article

What Is a Controlled Experiment?

Controlled Experiment

Example of a Controlled Experiment

Why Controlled Experiments Are Important

Are All Experiments Controlled?

Have a language expert improve your writing

Control Variables | What Are They & Why Do They Matter?

Table of contents

Control variables in experiments

Control variables in non-experimental research

Prevent plagiarism. Run a free check.

Random assignment

Standardized procedures

Statistical controls

Receive feedback on language, structure, and formatting

Cite this Scribbr article

Is this article helpful?

Pritha Bhandari

Control Group Definition and Examples

Control Group vs Experimental Group

Control Group vs Control Variable

Types of Control Groups

Control Group Examples

Negative Control and Placebo Group

Positive and Negative Controls

Related Posts

Positive Control vs Negative Control: Differences & Examples

Positive Control vs Negative Control: Key Terms

The Negative Control