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What Is a Controlled Experiment?

Definition and Example

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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.
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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

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• 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)

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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control experiment

Definition of control experiment

Examples of control experiment in a sentence, word history.

1848, in the meaning defined above

Dictionary Entries Near control experiment

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control freak

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“Control experiment.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/control%20experiment. Accessed 9 Nov. 2024.

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Experimental Method In Psychology

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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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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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.

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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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control experiment

• an experiment in which the variables are controlled so that the effects of varying one factor at a time may be observed.
• an experiment designed to check or correct the results of another experiment by removing the variable or variables operating in that other experiment. The comparison obtained is an indication or measurement of the effect of the variables concerned

Word History and Origins

Origin of control experiment 1

Example Sentences

He’s a pioneer of using randomized control experiments in economics — studying the long-term benefits of a $1 health intervention in Africa.

These remote control experiments are revealing new insights on the neural circuitry underlying social interactions, supporting previous work suggesting minds in sync are more cooperative, researchers report online May 10 in Nature Neuroscience.

The second group of stalks was placed simply in water, in order to serve for control experiment.

It has no idea of the need for what is called a control experiment.

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Controlled Experiment Meaning, Importance & Examples in Science

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A controlled experiment is a research method that tests a hypothesis by manipulating a specific variable, called the independent variable. Researchers keep all other factors constant (controlled) to see how the independent variable affects the outcome, or dependent variable. Control groups are used for comparison, so that any changes can be attributed to the independent variable. A controlled scientific experiment helps establish cause-and-effect relationships and reduces potential bias, leading to more reliable results.

Any study in psychology, sociology, natural sciences and humanities is impossible without conducting experiments. They confirm theories, test new drugs, and develop methods. Besides, students often have to control an experiment while learning.

A controlled scientific experiment — how does it proceed? What determines accuracy and reliability of results? What methods are used by  researchers? We will take a closer look at te definition of a controlled experiement. Read our guide to learn how to rule out mistakes and outside influences in your experiment. You will find many useful suggestions in this article, including tips on how to provide control, exclude an influence of extraneous factors and many more.

What Is a Controlled Experiment: Definition

A controlled experiment is testing under programmed conditions. It’s the main tool for testing advanced scientific theories. Validity and effectiveness of scientific research depend on the experiment’s accuracy. This method is actively used in medicine. It is also used in social and natural sciences, psychology, and biochemistry. There are two main parts of such an experiment:

• test factor
• experimental and control groups.

External influences on both groups must remain constant. Experimental group help researchers test substances or effects. Control group is not affected by this effect. Experimenter observes an influence of independent variables on dependent variables. Excluding extraneous influences ensures that you receive statistically valid results.

Importance of Control in an Experiment

Extraneous variables should not affect the course of an experiment. The role of a control in an experiment is to exclude external factors and ensure high internal validity of results. The main purpose is to eliminate outside influences. And also you should be able to maximally manage an effect of an independent variable. That makes it possible to exclude mistaken hypotheses about an observed effect. At the same time, it is necessary to ensure high internal validity. It means measuring the exact impact of an independent variable on a dependent one. External validity is an ability to transfer test results outside the research framework. Before doing such kind of an experiment, find out the difference between reliability and validity beforehand.

Examples of Controlled Experiments

Several interrelated variables can be examined in a single study. Controlled experiments explore relationships between people. They also explore altered states of consciousness or medication effects. Distribution of participants should be made randomly. It should be done to exclude an influence of external factors. Consider controlled experiment example in somnology. For example, the effect of sleep duration on concentration is being investigated. A study may include assessing different sleep duration effects. Dependent variable is an ability to concentrate as measured by cognitive tests results. Independent variable is sleep duration. Statistical measurement methods will determine changes in concentration of a person's attention depending on a sleep duration. So what is controlled in this experiment? Any external factors that may distort the relationship between independent and dependent variables .

Methods Used in Controlled Experiments

Choosing methods depends on controlled experiment conditions. Besides, a research on new medications is being conducted on volunteers. Consider these methods of forming samples of groups:

• Parallel Each object of research is assigned randomly. Everyone is influenced by factor being investigated.
• Crossover Each participant is influenced by independent variable in a random sequence.
• Cluster Previously established groups of objects are investigated.
• Factorial Distribution of objects influenced in group occurs randomly.

A laboratory test provides maximum experimental control. It allows you to standardize sampling conditions and reproduce identical conditions for subsequent experiments.

Randomized Controlled Experiment

It is necessary to see the difference between controlled experiments and quasi-experiments. The latter does not give a real picture of the phenomenon under study. Random assignments ensure that an experimental study is objective. Each subject receives an equal opportunity for experiment. Random selection allows you to compose a large population of participants in an experiment.  If principles of random distribution are violated, then we have quasi experiment . Its results can be predicted in advance and they are not reliable.

Control Group in an Experiment

Scientific research suggests the need to compare tests and experiments results with behavior of a control group. This is especially important when you conduct medical test on effects of treatments and new drugs. Comparison of control and experimental groups ’ well-being allows one to draw conclusions about taking some drug. In the control group, changes may also occur against the background of taking a placebo. Precise monitoring is essential to distinguish treatment outcomes from psychological factors.

Blinding in an Experiment

In medicine, the most reliable method is double-blind placebo-controlled experiment. When conducting it, not only participants, but also researchers do not know about distribution into groups. This ensures that all participants in experiment behave naturally. They eliminate bias.  Experimental group gets test drug during course of the study. If patients cannot do without treatment, they are given a drug of proven action. Control group gets a placebo, masking some real medicine.

Pros and Cons of a Controlled Experiment

The advantage of controlled experiment is:

• Demonstrable cause-and-effect relationship between an investigated factor and its influence on experimental group;
• Possibility to exclude external variables;
• Impact of the researcher’s bias is ruled out;
• Ability to study random groups of participants;
• Test results can be verified;
• Data can be combined with results from other studies.

These advantages are necessary to ensure an external validity of results.

The disadvantages are:

• Attempts to manage numerous variables is time-consuming and requires many precise data;
• There are huge numbers of variables and managing all variables is almost impossible;
• Researcher’s personal bias may intrude;
• Groups may not be comparable;
• Human response can be difficult to measure;
• Results may only apply to one situation and may be difficult to replicate.

Such tests have their disadvantages, so scientists and researchers should find a balance between the benefits it provides and the drawbacks it presents.

Bottom Line

Summing up, it is necessary to emphasize the relevance of control science experiments. Not all companies, nor all private researchers have opportunity to organize a large-scale experiment.  The theory confirmation in psychology, sociology, medicine, biochemistry is impossible without experimental research. Among these, it is the most reliable method for predicting possible outcomes. The experimental techniques limit the influence of extraneous effects. They also limit an observer's intervention during a test.

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FAQ About Controlled Experiments

1.why is a control group important in an experiment.

The purpose of a control group in an experiment is to provide a basis for comparing and analyzing effects of an investigated factor on participants in experimental group. It allows you to eliminate an impact of fatal external factors on independent and dependent variables. An experimental group is influenced by an investigated factor. At this time, the control group allows you to assess the progress of the ongoing processes.

2. What is the control in an experiment?

Scientific control in an experiment is necessary to minimize the extraneous variables' influence. It allows you to exclude an impact of an element that is not directly related to essence of a study. It helps focus the attention on the influence of an independent variable. Experimental and control groups comparison prevent errors. It also eliminates observer bias.

3. Why are hypotheses important to controlled experiments?

A controlled experiment's purpose is to confirm or disprove a particular hypothesis. It sets the research direction, allows you to choose methods and highlight important factors. It ensures accuracy of the result, and excludes extraneous influences. The bounded stage allows you to assess the effect of a variable on a control group. Hypotheses are important to experiments as a basis for creating a work plan.

4. What is the difference between a control group and an experimental group?

Experimental group is influenced by an investigated independent variable. This receives the treatment whose effect researchers wish to study. For example, experimenters are testing a new drug’s effectiveness. Whereas a control group does not receive this treatment. Both groups are recruiting volunteers with the same disease. They also should be identical in all other ways. To prevent control group from suffering from a lack of treatment, participants are given previously tested drugs. Experimenters monitor the effects of old and new drugs and compare them.

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Controlled Experiment – Meaning, Methods & Examples

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In a controlled experiment, the researcher manipulates one variable, the independent variable, while keeping all other variables constant to observe the effect on the dependent variable. This careful control forms the core of this methodology , eliminating the possibility of external factors influencing the outcome and thus allowing the researcher to draw stronger conclusions about cause-and-effect relationships.

Inhaltsverzeichnis

• 1 Controlled Experiment – In a Nutshell
• 2 Definition: Controlled experiment
• 3 Why is a controlled experiment important?
• 4 Methods used in controlled experiments
• 5 Problems with controlled experiments

Controlled Experiment – In a Nutshell

• Controlled experiments allow researchers to measure the effect of particular variables.
• A controlled experiment is used to divide population samples into control and experimental groups to estimate the effect of a critical variable.
• Control experiments can be carried out in different ways, including a disguised approach where participants are unaware of the investigated variable.

Definition: Controlled experiment

Experiments in research entail manipulating or changing an independent variable and assessing its effect on the dependent variable or outcome. All the variables except the independent variable are held constant in a controlled experiment. Researchers begin by defining the variables present to ensure objective measurement of the essential variables.

The variables held constant are known as control variables. Researchers can control variables by:

• Maintaining variables at a regulated level
• Analyzing variables for statistical purposes
• Using random tests in an experiment

Why is a controlled experiment important?

A controlled experiment ensures internal validity, essential for establishing cause and effect through experimentation.

You want to study the effects of music on productivity. You want to see if music improves people’s productivity.

• Your independent variable is music
• Your dependent variable is the hours that music listeners stay productive

Many factors can influence productivity. A controlled experiment is ideal to test whether listening to music affects productivity.

Extraneous variables – These are factors that you don’t measure but which may affect the dependent variable. You should eliminate the effects of these variables to create strong internal validity . Extraneous variables in this example include:

• The type of work
• The workplace environment
• The worker’s level of skill
• The worker’s level of income

In an uncontrolled experiment, these variables may affect a worker’s productivity, which makes it hard to assess the true impact of music on productivity.

Methods used in controlled experiments

Researchers use standard data collection practices to control variables in a study. The same tools should be used in a shared environment to test all the participants. A systematic approach is applied to change only the independent variable, for instance, the type of music.

Sampling methods can be used to manage other extraneous variables. You should select the most representative sample based on a criterion that considers all the essential characteristics, for example, using participants from a specific job description rather than short participants. Noting the extraneous variables in your study can help in further analysis.

Once the participants have been identified, they are grouped to test individual independent variables. The identifiable groups guide the implementation of controls in the experiment.

Control groups

A controlled experiment uses control groups . Researchers use control groups to test the effect of a given variable by comparing the control group with an experimental group. The control group is isolated from the experimental group to ensure the independent variable does not affect the control group.

In the music experiment, the participants can be placed in identical rooms for the duration of the study. The participants can be placed in one of two groups to determine the effect of music on productivity:

• A control group listening to jazz music and doing routine tasks
• An experimental group listening to classical music doing the same everyday tasks

The only difference between these groups is the type of music. All other characteristics are identical.

Random assignment

A random assignment prevents systematic differences between an experimental group and the control group. It involves placing the research subjects into random groups. Every participant has an equal opportunity to be placed in the experimental group.

Randomization is necessary for “true experiments”, as opposed to quasi-experiments, where the subjects are chosen based on a criterion.

The workers in our previous example can be assigned random numbers. Odd and even numbers can be grouped together to form experimental and control groups.

Random assignment ensures the two groups can be compared based on age, role, gender, and other observable traits. This enables comparison between the groups.

Masking (blinding)

Masking in a controlled experiment involves hiding some variables from the researchers and the subjects. Researchers often intentionally conceal the presence of certain variables in a study to prevent bias that may arise due to the participants’ awareness. Masking ensures strong internal validity by ensuring the participants behave naturally.

Researchers may unknowingly influence the behavior of participants. Other environmental cues may also nudge participants to behave in a particular way. Masking guarantees that no undue biases can affect the result of the study.

For the music study, a researcher can introduce other cues such as art or snack in the workplace. By disguising the experiment’s true purpose, the participants are unaware of the variable that the researcher is interested in.

Problems with controlled experiments

Controlled experiments may be an effective research tool. However, there are some challenges associated with its methodology

✘ Difficulties controlling all variables

Every research participant behaves in a certain way based on their beliefs, personality, attitudes, and other factors. This poses a problem for researchers attempting to hold the extraneous variables constant because there are too many variables. However, a researcher may limit the extraneous variables to manage their influence on the outcome.

✘ Risk of low external validity

Many controlled experiments do not apply to other contexts outside the experiment. A highly controlled experiment may lack real-world relevance as it considers a limited list of variables. Researchers need to consider the objectives of their experimental studies to prioritize control or applicability.

What is a controlled experiment?

A control experiment is a method of experimental research where only the independent variable is changed. The dependent variables are held constant in a controlled environment to isolate the effect of one variable.

What is a control group in a controlled experiment?

The control group in a controlled experiment is the group that doesn’t receive a variable. Experimental programs such as clinical trials use control groups in testing new drugs by comparing those who receive treatment and those who don’t.

When is masking used in a controlled experiment?

Sometimes, researchers may be concerned about the effect of a subject’s awareness on the findings of a research project. Masking is a deliberate action by researchers to hide some variables in a study as a measure to build strong internal validity .

Where are controlled experiments used?

Controlled experiments are commonly used in healthcare research. New treatment methods are tested on groups of patients to test the efficacy and other variables that may affect their effectiveness.

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Definition of controlled experiment noun from the Oxford Advanced Learner's Dictionary

controlled experiment

• Controlled experiments show how shore crabs of different sizes respond to ship noise.

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Fact check: Debunking weather modification claims

No one creates or steers hurricanes; the technology does not exist.

As the southeastern United States reels from the impact of two historic hurricanes, a large amount of disinformation about nonexistent weather manipulation technology is spreading across the internet, particularly on social media platforms.  

Below, NOAA identifies some of the inaccurate claims circulating online and provides science-based facts and information in response.

In 2013, NOAA completed the dual-polarization technology update to 122 radar sites throughout the country. This new advanced technology is helping federal weather forecasters more accurately track, assess and warn the public of approaching high-impact weather. (Image credit: NOAA)

ADDITIONAL RESOURCES

GFDL fact sheet on hurricanes and human induced climate change

NOAA’s state of the climate fact sheet on Atlantic hurricanes and climate change

AOML Hurricanes FAQ

NOAA Weather Modification Website on the NOAA Library

• CLAIM: The government is creating, strengthening and/or steering hurricanes into specific communities. 
• FACT:   No technology exists that can create, destroy, modify, strengthen or steer hurricanes in any way, shape or form. All hurricanes, including Helene and Milton , are natural phenomena that form on their own due to aligning conditions of the ocean and atmosphere. 
• CLAIM: NOAA modifies the weather.
• FACT: NOAA does not modify the weather, nor does it fund, participate in or oversee cloud seeding or any other weather modification activities. NOAA’s objective is to better understand and predict Earth’s systems, from the bottom of the seafloor to the surface of the sun. We are deepening our understanding and deploying new resources to improve forecasting and give communities earlier and more accurate warnings ahead of extreme weather events. NOAA is required by law* to track weather modification activities by others, including cloud seeding, but has no authority to regulate those activities. *The Weather Modification Reporting Act of 1972 ( 15 Code of Federal Regulations § 908 ) requires anyone who intends to engage in weather modification activities within the United States, including cloud seeding, to provide a report to the Administrator of NOAA at least 10 days prior to undertaking the activity. Those reports are filed via email and may be found on the NOAA Central Library website.
• CLAIM: The government is engaging in activities like cloud seeding to modify the weather. 
• FACT: NOAA does not fund or participate in cloud seeding or other weather modification projects. Cloud seeding is the only common weather modification activity currently practiced in the United States — typically by private companies in western mountain basins in winter in order to help generate snow in specific locations, or in the desert southwest to replenish water reservoirs in summer. The method has been used for decades in an effort to increase stored water in snowpack that melts in the spring to maintain adequate water supply. Decades ago, between 1962 and 1982, NOAA provided support for research into whether hurricane intensity could be modified, known as Project STORMFURY. The research was not successful in modifying hurricanes and STORMFURY was discontinued. NOAA has not attempted to modify hurricane intensity and participate in cloud seeding since. For more information on this project, visit this NOAA Atlantic Oceanographic & Meteorological Laboratory website .
• CLAIM: NEXRAD Doppler radars are being used to steer hurricanes and are targeting specific communities.
• FACT: Radars are tools for observation and are not able to direct the motion or intensity of air masses or storms. NEXRAD Doppler radars detect precipitation and the motion of the precipitation particles. The radar can determine an object’s location, shape, intensity and movement relative to the radar, but cannot alter or move those objects in any way. NEXRAD radars have been an essential weather forecasting tool since the 1990s and weather radars in general have been in use in the United States since the 1950s.
• CLAIM: Solar geoengineering made hurricanes Helene and Milton worse.
• FACT: Solar geoengineering, a theoretical practice which would modify the atmosphere to shade Earth’s surface by reflecting sunlight back into space, is not taking place at scale anywhere in the world. Geoengineering did not impact hurricanes Helene and Milton, let alone make them worse. The Earth’s warming atmosphere can cause hurricanes to intensify rapidly and carry more moisture, allowing them to dump higher amounts of rain. Record to near-record warm ocean temperatures across the Gulf of Mexico allowed hurricanes Helene and Milton to rapidly intensify. Natural steering currents in the upper atmosphere determine a storm’s path. 
• CLAIM: NOAA is conducting solar geoengineering.
• FACT: NOAA is not conducting solar geoengineering. NOAA studies the stratosphere and marine boundary layer with instruments on balloons and aircraft to help fill important gaps in our knowledge and inform decisions about the potential risks and benefits of solar geoengineering.
• CLAIM: NOAA is involved with projects like HAARP and SCOPEX that modify weather. 
• FACT:   NOAA is not associated with these projects, neither of which can modify the weather.  HAARP offsite link is a small National Science Foundation-funded facility in Gakona, Alaska, that conducts research on the ionosphere, 30 to 600 miles above the Earth’s surface. HAARP (High-frequency Active Auroral Research Program) is not capable of influencing local weather at Earth’s surface, let alone tropical cyclones thousands of miles away. The HAARP system is basically a large radio transmitter.  SCOPEX, offsite link run out of Harvard University, was a scientific research project to study the behavior of small amounts of aerosols in the stratosphere to advance the understanding of solar geoengineering. The proposed scientific research project ended in March 2024 before field experiments were conducted.

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