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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

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.

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• v.20(10); 2019 Oct 4

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.

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.

Further reading

Anderson C (2008) The End of Theory: the data deluge makes the scientific method obsolete. Wired (December 23, 2008)

Bacon F (1620, 2011) Novum Organum Scientiarum. Nabu Press

Baluška F, Gagliano M, Witzany G (2018) Memory and Learning in Plants. Springer Nature

Charlesworth AG, Seroussi U, Claycomb JM (2019) Next‐Gen learning: the C. elegans approach. Cell 177: 1674–1676

Eliezer Y, Deshe N, Hoch L, Iwanir S, Pritz CO, Zaslaver A (2019) A memory circuit for coping with impending adversity. Curr Biol 29: 1573–1583

Gagliano M, Renton M, Depczynski M, Mancuso S (2014) Experience teaches plants to learn faster and forget slower in environments where it matters. Oecologia 175: 63–72

Gagliano M, Vyazovskiy VV, Borbély AA, Grimonprez M, Depczynski M (2016) Learning by association in plants. Sci Rep 6: 38427

Katz M, Shaham S (2019) Learning and memory: mind over matter in C. elegans . Curr Biol 29: R365‐R367

Kováč L (2007) Information and knowledge in biology – time for reappraisal. Plant Signal Behav 2: 65–73

Kováč L (2008) Bioenergetics – a key to brain and mind. Commun Integr Biol 1: 114–122

Koshland DE Jr (1980) Bacterial chemotaxis in relation to neurobiology. Annu Rev Neurosci 3: 43–75

Lyon P (2015) The cognitive cell: bacterial behavior reconsidered. Front Microbiol 6: 264

Margulis L (2001) The conscious cell. Ann NY Acad Sci 929: 55–70

Maximillian N (2018) The Metaphysics of Science and Aim‐Oriented Empiricism. Springer: New York

Mazzocchi F (2015) Could Big Data be the end of theory in science? EMBO Rep 16: 1250–1255

Moore RS, Kaletsky R, Murphy CT (2019) Piwi/PRG‐1 argonaute and TGF‐β mediate transgenerational learned pathogenic avoidance. Cell 177: 1827–1841

Peirce CS (1877) The Fixation of Belief. Popular Science Monthly 12: 1–15

Pigliucci M (2009) The end of theory in science? EMBO Rep 10: 534

Popper K (1959) The Logic of Scientific Discovery. Routledge: London

Posner R, Toker IA, Antonova O, Star E, Anava S, Azmon E, Hendricks M, Bracha S, Gingold H, Rechavi O (2019) Neuronal small RNAs control behavior transgenerationally. Cell 177: 1814–1826

Russell B (1912) The Problems of Philosophy. Henry Holt and Company: New York

Scerri E (2006) The Periodic Table: It's Story and Significance. Oxford University Press, Oxford

Shapiro JA (2007) Bacteria are small but not stupid: cognition, natural genetic engineering and socio‐bacteriology. Stud Hist Philos Biol Biomed Sci 38: 807–818

Torday JS, Miller WB Jr (2016) Biologic relativity: who is the observer and what is observed? Prog Biophys Mol Biol 121: 29–34

Torday JS, Rehan VK (2017) Evolution, the Logic of Biology. Wiley: Hoboken

Torday JS, Miller WB Jr (2016) Phenotype as agent for epigenetic inheritance. Biology (Basel) 5: 30

Wasserstein RL, Lazar NA (2016) The ASA's statement on p‐values: context, process and purpose. Am Statist 70: 129–133

Yamada T, Yang Y, Valnegri P, Juric I, Abnousi A, Markwalter KH, Guthrie AN, Godec A, Oldenborg A, Hu M, Holy TE, Bonni A (2019) Sensory experience remodels genome architecture in neural circuit to drive motor learning. Nature 569: 708–713

Ladislav Kováč discussed the advantages and drawbacks of the inductive method for science and the logic of scientific discoveries 9 . Obviously, technological advances have enabled scientists to expand the borders of knowledge, and informatics allows us to objectively analyze ever larger data‐sets. It was the telescope that enabled Tycho Brahe, Johannes Kepler, and Galileo Galilei to make accurate observations and infer the motion of the planets. The microscope provided Robert Koch and Louis Pasteur insights into the microbial world and determines the nature of infectious diseases. Particle colliders now give us a glimpse into the birth of the Universe, while DNA sequencing and bioinformatics have enormously advanced biology's goal to understand the molecular basis of life.

However, Kováč also reminds us that Bayesian inferences and reasoning have serious drawbacks, as documented in the instructive example of Bertrand Russell's “inductivist turkey”, which collected large amounts of reproducible data each morning about feeding time. Based on these observations, the turkey correctly predicted the feeding time for the next morning—until Christmas Eve when the turkey's throat was cut 9 . In order to avoid the fate of the “inductivist turkey”, mankind should also rely on Popperian deductive science, namely formulating theories, concepts, and hypotheses, which are either confirmed or refuted via stringent experimentation and proper controls. Even if our scientific instruments provide us with objective data, we still need to apply our consciousness to evaluate and interpret such data. Moreover, before we start using our scientific instruments, we need to pose scientific questions. Therefore, as suggested by Albert Szent‐Györgyi, we need both Dionysian and Apollonian types of scientists 10 . Unfortunately, as was the case in Szent‐Györgyi's times, the Dionysians are still struggling to get proper support.

There have been pleas for reconciling philosophy and science, which parted ways owing to the rise of empiricism. This essay recognizes the centrality experiments and their controls for the advancement of scientific thought, and the attendant advance in philosophy needed to cope with many extant and emerging issues in science and society. We need a common “will” to do so. The rationale is provided herein, if only.

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 .

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• 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)

Table of contents

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

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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

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What’s Hot and Cooking In Scholarly Publishing

Understanding Experimental Controls

• Experimentation

Much of the training that scientists receive in graduate school is experiential, you learn how to do an experiment by working in a laboratory and performing experiments. In my opinion, not enough time and effort is devoted to understanding the philosophy and methods of experimental design.

An experiment without the proper controls is meaningless. Controls allow the experimenter to minimize the effects of factors other than the one being tested. It’s how we know an experiment is testing the thing it claims to be testing.

This goes beyond science — controls are necessary for any sort of experimental testing, no matter the subject area. This is often why so many bibliometric studies of the research literature are so problematic. Inadequate controls are often performed which fail to eliminate the effects of confounding factors, leaving the causality of any effect seen to be undetermined.

Novartis’ David Glass has put together the videos below, showing some of the basics of experimental validation and controls (Full disclosure: I was an editor on the first edition of David’s book on experimental design). These short videos offer quick lessons in positive and negative controls, as well as how to validate your experimental system.

These are great starting points, and I highly recommend Glass’ book, now in its second edition , if you want to dig deeper and understand the nuances of the different types of negative and positive controls, not to mention method and reagent controls, subject controls, assumption controls and experimentalist controls.

David Crotty

David Crotty is a Senior Consultant at Clarke & Esposito, a boutique management consulting firm focused on strategic issues related to professional and academic publishing and information services. Previously, David was the Editorial Director, Journals Policy for Oxford University Press. He oversaw journal policy across OUP’s journals program, drove technological innovation, and served as an information officer. David acquired and managed a suite of research society-owned journals with OUP, and before that was the Executive Editor for Cold Spring Harbor Laboratory Press, where he created and edited new science books and journals, along with serving as a journal Editor-in-Chief. He has served on the Board of Directors for the STM Association, the Society for Scholarly Publishing and CHOR, Inc., as well as The AAP-PSP Executive Council. David received his PhD in Genetics from Columbia University and did developmental neuroscience research at Caltech before moving from the bench to publishing.

7 Thoughts on "Understanding Experimental Controls"

We could add one more necessary control in this experiment–controlling for variability in individual response.

In the three videos, the experimenter may only detect differences between groups (or average differences). He is unable to detect changes in individuals. Some participants may be more sensitive to caffeine than others, some may show negative changes, and some may show no changes at all. If we take the blood pressure of participants before they drink coffee, we have a baseline measurement for all individuals. We also have a check on whether the experimenter was able to randomly assign participants to each treatment group.

In effect, each individual is their own control, with a before and after measurement. The experimenter is looking at the change in response of the individual rather than the average effect of the group. It is a much more sensitive way to structure and analyze experiments like this.

• By Phil Davis
• Nov 2, 2018, 8:57 AM

Agreed, these videos only skim the surface (his book goes into much greater detail about a much wider range of controls).

• By David Crotty
• Nov 2, 2018, 9:05 AM

Most experimenters who use random assignment to control and treatment groups have found that post-test only design works as well as pre-/post-test design.

• Nov 2, 2018, 10:01 AM

I don’t see how. By controlling for a potentially large source of variability—the individual participant—statistical tests become much more sensitive to changes than averaging all of that variability by group in a simple post-test design. Second, it is a check to see whether the randomization of participants into groups was successful. In many RTCs in the clinical sciences, there is recruitment bias, allowing for the sicker patients to be placed in the treatment group, for example.

• Nov 2, 2018, 12:55 PM

No mention of Institutional Review Board?! The IRB will raise Dr. Johnson’s own blood pressure.

And then there’s the issue of Dr. Johnson’s White Coat — that might trigger considerable individual variation. (My own blood pressure readings change markedly in the course of a visit to the doctor. )

• Nov 2, 2018, 4:59 PM

I believe that IRB approval is discussed in the video on system validation.

• Nov 2, 2018, 5:02 PM

Late to the debate, but I think those are wonderful. Maybe next Control Kitty will ask just how he assembled all those volunteers for his test to be representative and blinding to minimize bias. Were they self-selected? A bunch of caffeine habituated javaheads who responded to an ad in the coffee shop? I could see another video on randomization and sampling frames. I’m sure David Glass’s book goes into all that, but well, I have a shelf full of related books and I’m unlikely to benefit from and want to buy another. Unless maybe he hooks with another clever video or two. Go Kitty! Except, ~900 views! That’s sad. I might have sneak in citations to them. (I tend to get chastised by reviewers/editors for citing non-scholarly sources.) Something like this might slip under the editor’s radar: Glass, D. 2018. Experimental Design for Biologists: 1. System Validation. Video (4:06 minutes). YouTube. https://www.youtube.com/watch?v=qK9fXYDs–8 [Accessed November 11, 2018].

• By Chris Mebane
• Nov 12, 2018, 12:17 AM

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What Is a Control Variable? Definition and Examples

A control variable is any factor that is controlled or held constant during an experiment . For this reason, it’s also known as a controlled variable or a constant variable. 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.

Importance of Control Variables

Remember, the independent variable is the one you change, the dependent variable is the one you measure in response to this change, and the control variables are any other factors you control or hold constant so that they can’t influence the experiment. Control variables are important because:

• They make it easier to reproduce the experiment.
• The increase confidence in the outcome of the experiment.

For example, if you conducted an experiment examining the effect of the color of light on plant growth, but you didn’t control temperature, it might affect the outcome. One light source might be hotter than the other, affecting plant growth. This could lead you to incorrectly accept or reject your hypothesis. As another example, say you did control the temperature. If you did not report this temperature in your “methods” section, another researcher might have trouble reproducing your results. What if you conducted your experiment at 15 °C. Would you expect the same results at 5 °C or 35 5 °C? Sometimes the potential effect of a control variable can lead to a new experiment!

Sometimes you think you have controlled everything except the independent variable, but still get strange results. This could be due to what is called a “ confounding variable .” Examples of confounding variables could be humidity, magnetism, and vibration. Sometimes you can identify a confounding variable and turn it into a control variable. Other times, confounding variables cannot be detected or controlled.

Control Variable vs Control Group

A control group is different from a control variable. You expose a control group to all the same conditions as the experimental group, except you change the independent variable in the experimental group. Both the control group and experimental group should have the same control variables.

Control Variable Examples

Anything you can measure or control that is not the independent variable or dependent variable has potential to be a control variable. Examples of common control variables include:

• Duration of the experiment
• Size and composition of containers
• Temperature
• Sample volume
• Experimental technique
• Chemical purity or manufacturer
• Species (in biological experiments)

For example, consider an experiment testing whether a certain supplement affects cattle weight gain. The independent variable is the supplement, while the dependent variable is cattle weight. A typical control group would consist of cattle not given the supplement, while the cattle in the experimental group would receive the supplement. Examples of control variables in this experiment could include the age of the cattle, their breed, whether they are male or female, the amount of supplement, the way the supplement is administered, how often the supplement is administered, the type of feed given to the cattle, the temperature, the water supply, the time of year, and the method used to record weight. There may be other control variables, too. Sometimes you can’t actually control a control variable, but conditions should be the same for both the control and experimental groups. For example, if the cattle are free-range, weather might change from day to day, but both groups have the same experience. When you take data, be sure to record control variables along with the independent and dependent variable.

• Box, George E.P.; Hunter, William G.; Hunter, J. Stuart (1978). Statistics for Experimenters : An Introduction to Design, Data Analysis, and Model Building . New York: Wiley. ISBN 978-0-471-09315-2.
• Giri, Narayan C.; Das, M. N. (1979). Design and Analysis of Experiments . New York, N.Y: Wiley. ISBN 9780852269145.
• Stigler, Stephen M. (November 1992). “A Historical View of Statistical Concepts in Psychology and Educational Research”. American Journal of Education . 101 (1): 60–70. doi: 10.1086/444032

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Experimental Method In Psychology

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.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

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

On This Page:

The experimental method involves the manipulation of variables to establish cause-and-effect relationships. The key features are controlled methods and the random allocation of participants into controlled and experimental groups .

What is an Experiment?

An experiment is an investigation in which a hypothesis is scientifically tested. An independent variable (the cause) is manipulated in an experiment, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

An advantage is that experiments should be objective. The researcher’s views and opinions should not affect a study’s results. This is good as it makes the data more valid  and less biased.

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 conditions (not necessarily a laboratory) where accurate measurements are possible.

The researcher uses a standardized procedure to determine where the experiment will take place, at what time, with which participants, and in what circumstances.

Participants are randomly allocated to each independent variable group.

Examples are Milgram’s experiment on obedience and  Loftus and Palmer’s car crash study .

• Strength : It is easier to replicate (i.e., copy) a laboratory experiment. This is because a standardized procedure is used.
• Strength : They allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.
• Limitation : The artificiality of the setting may produce unnatural behavior that does not reflect real life, i.e., low ecological validity. This means it would not be possible to generalize the findings to a real-life setting.
• Limitation : Demand characteristics or experimenter effects may bias the results and become confounding variables .

2. Field Experiment

A field experiment is a research method in psychology that takes place in a natural, real-world setting. It is similar to a laboratory experiment in that the experimenter manipulates one or more independent variables and measures the effects on the dependent variable.

However, in a field experiment, the participants are unaware they are being studied, and the experimenter has less control over the extraneous variables .

Field experiments are often used to study social phenomena, such as altruism, obedience, and persuasion. They are also used to test the effectiveness of interventions in real-world settings, such as educational programs and public health campaigns.

An example is Holfing’s hospital study on obedience .

• Strength : behavior in a field experiment is more likely to reflect real life because of its natural setting, i.e., higher ecological validity than a lab experiment.
• Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied. This occurs when the study is covert.
• Limitation : There is less control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

3. Natural Experiment

A natural experiment in psychology is a research method in which the experimenter observes the effects of a naturally occurring event or situation on the dependent variable without manipulating any variables.

Natural experiments are conducted in the day (i.e., real life) environment of the participants, but here, the experimenter has no control over the independent variable as it occurs naturally in real life.

Natural experiments are often used to study psychological phenomena that would be difficult or unethical to study in a laboratory setting, such as the effects of natural disasters, policy changes, or social movements.

For example, Hodges and Tizard’s attachment research (1989) compared the long-term development of children who have been adopted, fostered, or returned to their mothers with a control group of children who had spent all their lives in their biological families.

Here is a fictional example of a natural experiment in psychology:

Researchers might compare academic achievement rates among students born before and after a major policy change that increased funding for education.

In this case, the independent variable is the timing of the policy change, and the dependent variable is academic achievement. The researchers would not be able to manipulate the independent variable, but they could observe its effects on the dependent variable.

• Strength : behavior in a natural experiment is more likely to reflect real life because of its natural setting, i.e., very high ecological validity.
• Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied.
• Strength : It can be used in situations in which it would be ethically unacceptable to manipulate the independent variable, e.g., researching stress .
• Limitation : They may be more expensive and time-consuming than lab experiments.
• Limitation : There is no control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

Key Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. EVs should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of participating in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

Science In Doses

Science-based instructional sketches for aspiring researchers and passionate sustainability advocates.

• Parvathi JR
• Aug 6, 2021

What are Blank, Control, Standard and Test in research?

Updated: Nov 9, 2023

This is one of the most common and perplexing question in research. Perplexing because many don't figure out the need for a standard or the difference between a blank and a control.

Imagine you working on a plant extract that shows anticancerous property

Can you work without the 'particular' plant extract?

- That is where the test comes in!

How can you say that the plant extract contains potential substance with anticancerous property?

- That is where the control comes in!

How can you say that this substance has great anticancerous potential?

- That is where the standard comes in!

How can you be sure that this is even a plant extract?

- That is where the blank comes in!

In many of the above questions a definite answer is obtained only when we do a comparative between blank, control, standard and test. All these can be broadly classified under "samples" which are a set of factors that is required to prove the research objective.

Blank is anything without the special ingredient you want to study

Control is anything which contains a particular amount of the special ingredient you want to study

Standard is anything which contains the special ingredient in a know quantity

Test is anything that contain the special ingredient but the quantity is unknown

Comparing the result of :

Test and Standard help in calculating the quantity of special ingredient in the test

Test and Control confirms the identity of the special ingredient

Test and Control confirms the presence of the special ingredient

• Research-In-Doses

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Methodology

• What Is a Controlled Experiment? | Definitions & Examples

What Is a Controlled Experiment? | Definitions & Examples

Published on April 19, 2021 by Pritha Bhandari . 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:

• 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 randomization (e.g., using a random order of tasks).

Table of contents

Why does control matter in experiments, methods of control, problems with controlled experiments, other interesting articles, 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. Strong validity also helps you avoid research biases , particularly ones related to issues with generalizability (like sampling bias and selection bias .)

• Your independent variable is the color 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.

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You can control some variables by standardizing your data collection procedures. All participants should be tested in the same environment with identical materials. Only the independent variable (e.g., ad color) 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 color 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 (e.g., a placebo to control for a placebo effect ), 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.

To test the effect of colors in advertising, each participant is placed in one of two groups:

• 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 and selection bias 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 and is critical for avoiding several types of research bias .

Sometimes, researchers may unintentionally encourage participants to behave in ways that support their hypotheses , leading to observer bias . In other cases, cues in the study environment may signal the goal of the experiment to participants and influence their responses. These are called demand characteristics . If participants behave a particular way due to awareness of being observed (called a Hawthorne effect ), your results could be invalidated.

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.

You use an online survey form to present the advertisements to participants, and you leave the room while each participant completes the survey on the computer so that you can’t tell which condition each participant was in.

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 generalized 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 prioritize control or generalizability in your experiment.

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.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

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

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

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

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

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

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

When designing the experiment, you decide:

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

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

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What is the purpose of having a control in an experiment?

I can best explain through example. Let's say that you are sea sick. You take two remedies, an acupressure bracelet and 50 milligrams of meclizine. You quickly start to feel better, but which of the two factors made the difference? The bracelet? The meclizine? A combination of both? This is a small-scale example, but in the fields of science it becomes important to know what causes a certain affect. This is the reason that we use controls.-Akilae

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The purpose of having a control in an experiment is to provide a baseline comparison for the results obtained from the experimental group. By comparing the results of the experimental group to the control group, researchers can determine the effect of the variables being tested and ensure that any observed changes are not due to external factors.

Having a control in an experiment allows you to see what happens when no variables are changed. If you do not have a control, you do not have anything to compare your results with after changing variables of the experiment.

Add your answer:

What is the purpose of the control group?

The purpose of a control group is to show what would happen under normal conditions. It serves as a comparison to the results you receive from the manipulation of the independent variable on the dependent variable. If a control group is present in an experiment, one can be more certain that the independent variable is really responsible for the observations.

Why is control important in an experiment?

Control is important in an experiment to eliminate the influence of variables other than the one being tested. By having a control group that is not exposed to the experimental treatment, researchers can accurately gauge the true effect of the independent variable on the outcome. This allows for valid and reliable conclusions to be drawn from the experiment.

In an experiment there are two groups. Which group is not changed in any way?

The control group is not changed in any way during the experiment. It serves as a baseline for comparison with the group that is exposed to the experimental manipulation.

What do you call something you experiment on?

Something that is experimented on is typically referred to as a &quot;subject&quot; or a &quot;test subject.&quot;

What is purpose of control?

Internal controls can help companies reduce errors, which can help them save money and protect their reputations. Employee training is an example of an internalcontrolthat can reduce errors.

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Pickleball Paddle Size & Weight Guide

Pickleball is one of the fastest-growing sports in the world. It's easy to learn, fun to play, and great for all ages. But to play your best, you need the right equipment. One of the most important pieces of gear is the pickleball paddle. Choosing the right paddle size and weight can make a big difference in your game.

In this article, we'll explore:

• Pickleball paddle size
• Pickleball handle length and grip size
• Pickleball paddle weight

By the end, you'll know how to choose the perfect paddle for your playing style and preferences.

Understanding Pickleball Paddle Size

When we talk about pickleball paddle size, we're referring to the dimensions of the paddle. This includes:

• Length: The standard length for a pickleball paddle is usually around 15-16 inches.
• Width: Most paddles are about 7-8 inches wide.
• Thickness: Paddles can vary in thickness, which can affect the feel and performance.

The size of your paddle can impact your gameplay in several ways:

• Reach: A longer paddle can help you reach shots that are further away.
• Control: A wider paddle can offer more surface area for hitting the ball, which can improve control.
• Power: The thickness of the paddle can influence how powerful your shots are.

Pickleball Handle Length and Grip Size

Choosing the right handle length and grip size is crucial for your comfort and control on the court. Here’s why:

• Handle Length: The standard handle length for pickleball paddles ranges from 4 to 5.5 inches. A longer handle can provide more leverage and reach, while a shorter handle offers better maneuverability.
• Grip Size: Common grip sizes range from 4 to 4.5 inches in circumference. A proper grip size ensures you can hold the paddle comfortably without straining your hand.

Here’s how handle length and grip size can affect your game:

• Control: A smaller grip size allows for finer control and quicker wrist action, which is ideal for players who rely on spin and precision.
• Comfort: A grip that’s too large or too small can cause discomfort and lead to blisters or hand fatigue. Choosing the right size helps prevent these issues.
• Injury Prevention: Using a paddle with the right handle length and grip size can reduce the risk of injuries such as tennis elbow or wrist strain.

To choose the right handle length and grip size, consider your hand size and playing style:

• Measure Your Hand: Measure from the tip of your ring finger to the bottom of your palm. This measurement can help you find a grip that fits well.
• Try Different Sizes: Experiment with different handle lengths and grip sizes to see what feels most comfortable and gives you the best control.

Pickleball Paddle Weight Guide

Pickleball paddles come in various weight categories, each offering different advantages:

• Lightweight (6.5 - 7.2 oz): These paddles are easy to maneuver, making them ideal for quick reactions and fast play. However, they may offer less power.
• Mid-weight (7.3 - 8.4 oz): These paddles strike a balance between control and power, making them a popular choice for many players.
• Heavyweight (8.5 - 9.5 oz): Heavier paddles provide more power but can be harder to control and may cause fatigue over long periods of play.

Here’s how paddle weight affects your game:

• Swing Speed: Lighter paddles allow for faster swings, which can be beneficial for quick volleys and defensive shots.
• Control: Mid-weight paddles offer a good balance, providing both control and power for a versatile playing style.
• Power: Heavier paddles generate more power, making them suitable for players who rely on strong, aggressive shots.

To choose the right paddle weight, consider your playing style and physical strength:

• Assess Your Strength: If you have less upper body strength, a lighter paddle may be easier to handle.
• Consider Your Style: If you play a fast, defensive game, a lightweight paddle might be best. For a powerful, aggressive game, consider a heavier paddle.

Ready to find your perfect paddle? Check out our full range of paddles or explore our top picks like the Mach 2 Forza Pickleball Paddle and the Forza Mach 1 Pickleball Paddle .

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