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In a randomized and controlled psychology experiment , the researchers are examining the impact of an experimental condition on a group of participants (does the independent variable 'X' cause a change in the dependent variable 'Y'?). To determine cause and effect, there must be at least two groups to compare, the experimental group and the control group.
The participants who are in the experimental condition are those who receive the treatment or intervention of interest. The data from their outcomes are collected and compared to the data from a group that did not receive the experimental treatment. The control group may have received no treatment at all, or they may have received a placebo treatment or the standard treatment in current practice.
Comparing the experimental group to the control group allows researchers to see how much of an impact the intervention had on the participants.
Imagine that you want to do an experiment to determine if listening to music while working out can lead to greater weight loss. After getting together a group of participants, you randomly assign them to one of three groups. One group listens to upbeat music while working out, one group listens to relaxing music, and the third group listens to no music at all. All of the participants work out for the same amount of time and the same number of days each week.
In this experiment, the group of participants listening to no music while working out is the control group. They serve as a baseline with which to compare the performance of the other two groups. The other two groups in the experiment are the experimental groups. They each receive some level of the independent variable, which in this case is listening to music while working out.
In this experiment, you find that the participants who listened to upbeat music experienced the greatest weight loss result, largely because those who listened to this type of music exercised with greater intensity than those in the other two groups. By comparing the results from your experimental groups with the results of the control group, you can more clearly see the impact of the independent variable.
When it comes to using experimental groups in a psychology experiment, there are a few important things to know:
Experiments play an important role in the research process and allow psychologists to investigate cause-and-effect relationships between different variables. Having one or more experimental groups allows researchers to vary different levels or types of the experimental variable and then compare the effects of these changes against a control group. The goal of this experimental manipulation is to gain a better understanding of the different factors that may have an impact on how people think, feel, and act.
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Steingrimsdottir HS, Arntzen E. On the utility of within-participant research design when working with patients with neurocognitive disorders . Clin Interv Aging. 2015;10:1189-1200. doi:10.2147/CIA.S81868
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Kim H. Statistical notes for clinical researchers: Analysis of covariance (ANCOVA) . Restor Dent Endod . 2018;43(4):e43. doi:10.5395/rde.2018.43.e43
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By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."
Statistics By Jim
Making statistics intuitive
By Jim Frost 3 Comments
A control group in an experiment does not receive the treatment. Instead, it serves as a comparison group for the treatments. Researchers compare the results of a treatment group to the control group to determine the effect size, also known as the treatment effect.
Imagine that a treatment group receives a vaccine and it has an infection rate of 10%. By itself, you don’t know if that’s an improvement. However, if you also have an unvaccinated control group with an infection rate of 20%, you know the vaccine improved the outcome by 10 percentage points.
By serving as a basis for comparison, the control group reveals the treatment’s effect.
Related post : Effect Sizes in Statistics
Most experiments include a control group and at least one treatment group. In an ideal experiment, the subjects in all groups start with the same overall characteristics except that those in the treatment groups receive a treatment. When the groups are otherwise equivalent before treatment begins, you can attribute differences after the experiment to the treatments.
Randomized controlled trials (RCTs) assign subjects to the treatment and control groups randomly. This process helps ensure the groups are comparable when treatment begins. Consequently, treatment effects are the most likely cause for differences between groups at the end of the study. Statisticians consider RCTs to be the gold standard. To learn more about this process, read my post, Random Assignment in Experiments .
Observational studies either can’t use randomized groups or don’t use them because they’re too costly or problematic. In these studies, the characteristics of the control group might be different from the treatment groups at the start of the study, making it difficult to estimate the treatment effect accurately at the end. Case-Control studies are a specific type of observational study that uses a control group.
For these types of studies, analytical methods and design choices, such as regression analysis and matching, can help statistically mitigate confounding variables. Matching involves selecting participants with similar characteristics. For each participant in the treatment group, the researchers find a subject with comparable traits to include in the control group. To learn more about this type of study and matching, read my post, Observational Studies Explained .
Control groups are key way to increase the internal validity of an experiment. To learn more, read my post about internal and external validity .
Randomized versus non-randomized control groups are just several of the different types you can have. We’ll look at more kinds later!
Related posts : When to Use Regression Analysis
Suppose we want to determine whether regular vitamin consumption affects the risk of dying. Our experiment has the following two experimental groups:
In this experiment, we randomly assign subjects to the two groups. Because we use random assignment, the two groups start with similar characteristics, including healthy habits, physical attributes, medical conditions, and other factors affecting the outcome. The intentional introduction of vitamin supplements in the treatment group is the only systematic difference between the groups.
After the experiment is complete, we compare the death risk between the treatment and control groups. Because the groups started roughly equal, we can reasonably attribute differences in death risk at the end of the study to vitamin consumption. By having the control group as the basis of comparison, the effect of vitamin consumption becomes clear!
Researchers can use different types of control groups in their experiments. Earlier, you learned about the random versus non-random kinds, but there are other variations. You can use various types depending on your research goals, constraints, and ethical issues, among other things.
The group introduces a condition that the researchers expect won’t have an effect. This group typically receives no treatment. These experiments compare the effectiveness of the experimental treatment to no treatment. For example, in a vaccine study, a negative control group does not get the vaccine.
Positive control groups typically receive a standard treatment that science has already proven effective. These groups serve as a benchmark for the performance of a conventional treatment. In this vein, experiments with positive control groups compare the effectiveness of a new treatment to a standard one.
For example, an old blood pressure medicine can be the treatment in a positive control group, while the treatment group receives the new, experimental blood pressure medicine. The researchers want to determine whether the new treatment is better than the previous treatment.
In these studies, subjects can still take the standard medication for their condition, a potentially critical ethics issue.
Placebo control groups introduce a treatment lookalike that will not affect the outcome. Standard examples of placebos are sugar pills and saline solution injections instead of genuine medicine. The key is that the placebo looks like the actual treatment. Researchers use this approach when the recipients’ belief that they’re receiving the treatment might influence their outcomes. By using placebos, the experiment controls for these psychological benefits. The researchers want to determine whether the treatment performs better than the placebo effect.
Learn more about the Placebo Effect .
If the subject’s awareness of their group assignment might affect their outcomes, the researchers can use a blinded experimental design that does not tell participants their group membership. Typically, blinded control groups will receive placebos, as described above. In a double-blinded control group, both subjects and researchers don’t know group assignments.
When there is a waitlist to receive a new treatment, those on the waitlist can serve as a control group until they receive treatment. This type of design avoids ethical concerns about withholding a better treatment until the study finishes. This design can be a variation of a positive control group because the subjects might be using conventional medicines while on the waitlist.
When historical data for a comparison group exists, it can serve as a control group for an experiment. The group doesn’t exist in the study, but the researchers compare the treatment group to the existing data. For example, the researchers might have infection rate data for unvaccinated individuals to compare to the infection rate among the vaccinated participants in their study. This approach allows everyone in the experiment to receive the new treatment. However, differences in place, time, and other circumstances can reduce the value of these comparisons. In other words, other factors might account for the apparent effects.
December 19, 2021 at 9:17 am
Thank you very much Jim for your quick and comprehensive feedback. Extremely helpful!! Regards, Arthur
December 17, 2021 at 4:46 pm
Thank you very much Jim, very interesting article.
Can I select a control group at the end of intervention/experiment? Currently I am managing a project in rural Cambodia in five villages, however I did not select any comparison/control site at the beginning. Since I know there are other villages which have not been exposed to any type of intervention, can i select them as a control site during my end-line data collection or it will not be a legitimate control? Thank you very much, Arthur
December 18, 2021 at 1:51 am
You might be able to use that approach, but it’s not ideal. The ideal is to have control groups defined at the beginning of the study. You can use the untreated villages as a type of historical control groups that I talk about in this article. Or, if they’re awaiting to receive the intervention, it might be akin to a waitlist control group.
If you go that route, you’ll need to consider whether there was some systematic reason why these villages have not received any intervention. For example, are the villages in question more remote? And, if there is a systematic reason, would that affect your outcome variable? More generally, are they systematically different? How well do the untreated villages represent your target population?
If you had selected control villages at the beginning, you’d have been better able to ensure there weren’t any systematic differences between the villages receiving interventions and those that didn’t.
If the villages that didn’t receive any interventions are systematically different, you’ll need to incorporate that into your interpretation of the results. Are they different in ways that affect the outcomes you’re measuring? Can those differences account for the difference in outcomes between the treated and untreated villages? Hopefully, you’d be able to measure those differences between untreated/treated villages.
So, yes, you can use that approach. It’s not perfect and there will potentially be more things for you to consider and factor into your conclusions. Despite these drawbacks, it’s possible that using a pseudo control group like that is better than not doing that because at least you can make comparisons to something. Otherwise, you won’t know whether the outcomes in the intervention villages represent an improvement! Just be aware of the extra considerations!
Best of luck with your research!
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6 February 2023
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The independent variable is the thing the researchers are testing. They are trying to determine whether it’s responsible for any change that occurs in the experiment. The research control group is key for this as it allows them to isolate the independent variable’s effect on the experiment.
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Splitting the audience you’re testing into two identical groups will give you a control group and an experimental group.
Nothing will change for the control group during the research. For example, this group would receive a placebo in pharmaceutical research.
In contrast, one key variable changes for the experimental group. In a pharmaceutical experiment, researchers might administer a different drug. In advertising research, this might involve increasing the experimental group’s exposure to ads.
When evaluating the results, researchers will compare those obtained from the experimental group against the control group. The control group is the baseline.
In research where the two groups are truly identical, seeing different results between the groups suggests they were caused by the independent variable—the only thing that changed.
Examples of control groups in research exist in a wide range of business contexts. For example:
You want to test whether a 15% loyalty discount for repeat purchases would positively impact retention and revenue. So, you send a discount email to 50% of your customers who were randomly selected. The other 50% of customers are your control group.
You want to test whether a personal sales call will increase your chance of a sales conversion. You add this step to your existing nurturing campaign for a randomly selected portion of leads. Those who don’t receive a phone call are your control group.
You want to test whether different product packaging can change brand perceptions. To do this, you change the packaging for a randomly selected portion of customers. Customers who receive the same packaging as before are your control group. Sending a survey to all customers about their brand perceptions before and after the experiment will reveal the impact of the new packaging.
These are just some of the countless examples of control groups. Perhaps the most well-known example is in the medical field, where placebos treatments are used. Control groups receive placebo treatments under the exact same conditions as the experimental group to determine the treatment’s effects.
Control groups matter in research because they act as the benchmark to establish your results’ validity . They enable you to compare the results you see in your experimental group and determine if the variable you changed caused a different outcome.
Control groups and experimental groups should be identical in their makeup and environment in every possible way. You’ll be able to draw more definitive conclusions as long as the research process is identical for both groups. In other words, working with control groups improves your research’s internal validity .
Control groups are most common in experimental research, where you’re trying to determine the impact of a variable you’re changing. You split your research group into two groups that are as identical as possible. One receives a placebo, for example, while the other receives a treatment.
In this environment, the identical makeup of the group is essential. The most common way to accomplish this is by randomly splitting the group in two and ensuring that any variables you’re not testing remain the same throughout the research process.
You can also conduct experiments with multiple control groups. For example, when testing new ad messaging, the split between two control groups and one experimental group may be as follows:
Control group 1 receives no advertising
Control group 2 receives the existing advertising
Control group 3 receives the new ad messaging
This more complex type of experiment can test both the overall impact of ads and how much of that impact you could attribute to the new messaging.
Control groups are less common in non-experimental research but can still be useful. They most commonly occur in the following process designs:
In this research process, every person in the experimental group is matched to one other person based on their environmental and demographic similarities.
This is most common when randomly selecting two groups on a broader scale would not result in them being equal. It can help you ensure that the control group or individual continues to act as the baseline for the variable you are studying.
This is where multiple groups are part of the research, but they are not randomly assigned to test and control conditions.
Quasi-experimental design is most common when the groups you are studying already exist, like customers being shown new ad messaging versus non-customers. The control group in this example is made up of your non-customers, as the variable did not change for them.
While control groups tend to be similar across research contexts, they generally fall into two categories: negative and positive control groups.
The independent variable does not change in a negative control group. This group represents the true status quo, and you would test the experimental group against it.
Examples of negative control groups include many of the experiments listed above, like only changing product packaging or only offering a discount for one group of customers.
In positive control groups, the independent variable is changed where it is already known to have an effect. You would compare this group’s results against those from the experimental group receiving a variation of the same independent variable. This would enable you to determine if the effect changes.
In the example of a multi-control group experiment seen above, control group 1 (receiving no advertising) is a negative control group, while control group 2 (receiving the current level of advertising) is a positive control group.
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Einfluss der Wärmebehandlung am distalen Ende auf die Eigenschaften von thermisch aktivierten NiTi-Bogendrähten
The aim of this study was to evaluate the extent of property changes caused by heating the distal portion of heat-activated nickel–titanium (NiTi) wires.
Forty preformed heat-activated NiTi archwires (3M Unitek, Monrovia, CA, USA) with a nominal cross-section of 0.018″ were used in this study. The archwires were divided into a control group, not submitted to heat treatment and, thus, maintaining the as-received properties, and an experimental group, in which the archwires were submitted to heat treatment for distal bending at one end. Wire segments of control and experimental groups were submitted to differential scanning calorimetry (DSC) and Vickers microhardness measurements.
The DSC results suggest local recrystallization and precipitate dissolution at the heat-treated tip, which decreases as the distance to the wire’s tip increases. Vickers microhardness tests revealed significant changes for distances between 6 and 8 mm from the wire’s tip. Heating the distal portion of heat-activated NiTi archwires should be performed with care since this clinical procedure may compromise the performance of these wires to a distance of 8 mm from the archwire end.
Heat treatment for distal bending in heat-activated NiTi archwires may be performed, with little impact on the areas adjacent to heat treatment. In cases presenting molars requiring significant orthodontic corrections, it should be preferred to apply other techniques to avoid archwire sliding, such as crimpable stops, or to have flame control to avoid placing a heat-treated section in the tubes of these molars.
Ziel dieser Studie war es, das Ausmaß der Eigenschaftsveränderungen zu bewerten, die durch das Erhitzen des distalen Abschnitts von thermisch aktivierten NiTi(Nickel-Titan)-Drahtbögen verursacht werden.
Vierzig vorgeformte thermisch aktivierte NiTi-Bogendrähte (3M Unitek, Monrovia/CA, USA) mit einem nominalen Querschnitt von 0,018″ wurden in dieser Studie verwendet. Die Bogendrähte wurden unterteilt in eine Kontrollgruppe, die keiner Wärmebehandlung unterzogen wurde und somit die Eigenschaften im ursprünglichen Zustand beibehielt, und in eine Versuchsgruppe, bei der die Bogendrähte am distalen Ende einer Wärmebehandlung unterzogen wurden. Drahtsegmente der Kontroll- und Versuchsgruppen wurden einer differenziellen Scanning-Kalorimetrie (DSC) und einer Mikrohärteprüfung nach Vickers unterzogen.
Die DSC-Ergebnisse deuten auf eine lokale Rekristallisation und Ausfällungsauflösung am wärmebehandelten Ende hin, die mit zunehmender Entfernung zur Drahtspitze abnimmt. Vickers-Härteprüfungen zeigten signifikante Veränderungen für Entfernungen zwischen 6 und 8 mm von der Drahtspitze. Das Erhitzen des distalen Abschnitts von thermisch aktivierten NiTi-Bogendrähten sollte mit Vorsicht durchgeführt werden, da dieses klinische Verfahren die Leistung dieser Drähte bis zu 8 mm vom Bogendrahtende beeinträchtigen kann.
Die Wärmebehandlung für die distale Biegung bei thermisch aktivierten NiTi-Bogendrähten kann durchgeführt werden, mit geringem Einfluss auf die benachbarten Bereiche der Wärmebehandlung. In Fällen mit Molaren, die signifikante kieferorthopädische Korrekturen erfordern, sollten bevorzugt andere Techniken angewendet werden, um ein Rutschen des Bogendrahts zu vermeiden, wie z. B. klemmbare Stops, oder die Erwärmung so unter Kontrolle zu halten, dass vermieden wird, einen wärmebehandelten Abschnitt in die Röhrchen dieser Molaren einzusetzen.
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Department of Restorative Dentistry (ODR), Division of Orthodontics, Faculty of Dentistry, Federal University of Minas Gerais, Av. Pres. Antônio Carlos 6627, 31270-901, Belo Horizonte, Pampulha, MG, Brazil
Janaína de Oliveira Abrahão D.D.S., Rodrigo Hermont Cançado D.D.S., M.Sc., PhD, Esdras de Campos França D.D.S., M.Sc., PhD, Felipe Weidenbach Degrazia D.D.S., M.Sc., PhD & Leniana Santos Neves D.D.S., M.Sc., PhD
Department of Metallurgical and Materials Engineering, School of Engineering, Federal University of Minas Gerais, Belo Horizonte, Brazil
Leandro de Arruda Santos M.Sc., PhD & Pedro Damas Resende M.Sc., PhD
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Janaína de Oliveira Abrahão: conceptualization, data curation, formal analysis, investigation, methodology, writing original draft; Rodrigo Hermont Cançado: data curation, methodology, project administration, supervision, writing—review and editing; Esdras de Campos França: conceptualization, data curation, formal analysis, investigation, methodology; Leandro de Arruda Santos: data curation, methodology, resources, software, supervision; Pedro Damas Resende: data curation, methodology, investigation, methodology, writing review; Felipe Weidenbach Degrazia: validation, visualization, writing review and editing; Leniana Santos Neves: conceptualization, data curation, methodology, project administration, validation, writing original draft.
Correspondence to Felipe Weidenbach Degrazia D.D.S., M.Sc., PhD .
Conflict of interest.
J. de Oliveira Abrahão, R. Hermont Cançado, E. de Campos França, L. de Arruda Santos, P. Damas Resende, F. Weidenbach Degrazia and L. Santos Neves declare that they have no competing interests.
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de Oliveira Abrahão, J., Hermont Cançado, R., de Campos França, E. et al. Influence of distal-end heat treatment in the properties of heat-activated NiTi archwires. J Orofac Orthop (2024). https://doi.org/10.1007/s00056-024-00547-w
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Received : 08 November 2023
Accepted : 07 July 2024
Published : 27 August 2024
DOI : https://doi.org/10.1007/s00056-024-00547-w
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In research, the control group is the one not exposed to the variable of interest (the independent variable) and provides a baseline for comparison. The experimental group, on the other hand, is exposed to the independent variable. Comparing results between these groups helps determine if the independent variable has a significant effect on the outcome (the dependent variable).
The control group and experimental group are compared against each other in an experiment. The only difference between the two groups is that the independent variable is changed in the experimental group. The independent variable is "controlled", or held constant, in the control group. A single experiment may include multiple experimental ...
A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn't receive the experimental treatment.. However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group's outcomes before and after a treatment (instead of comparing outcomes between different groups).
The experimental group and the control group should be as similar as possible except for one factor, called a variable, which is changed. One variable can be added, taken away, or changed to ...
Table of Contents control group, the standard to which comparisons are made in an experiment.Many experiments are designed to include a control group and one or more experimental groups; in fact, some scholars reserve the term experiment for study designs that include a control group. Ideally, the control group and the experimental groups are identical in every way except that the experimental ...
Three types of experimental designs are commonly used: 1. Independent Measures. Independent measures design, also known as between-groups, is an experimental design where different participants are used in each condition of the independent variable. This means that each condition of the experiment includes a different group of participants.
A control group is typically thought of as the baseline in an experiment. In an experiment, clinical trial, or other sort of controlled study, there are at least two groups whose results are compared against each other. The experimental group receives some sort of treatment, and their results are compared against those of the control group ...
Positive control groups: In this case, researchers already know that a treatment is effective but want to learn more about the impact of variations of the treatment.In this case, the control group receives the treatment that is known to work, while the experimental group receives the variation so that researchers can learn more about how it performs and compares to the control.
A control group is not the same thing as a control variable. A control variable or controlled variable is any factor that is held constant during an experiment. Examples of common control variables include temperature, duration, and sample size. The control variables are the same for both the control and experimental groups.
To test its effectiveness, you run an experiment with a treatment and two control groups. The treatment group gets the new pill. Control group 1 gets an identical-looking sugar pill (a placebo). Control group 2 gets a pill already approved to treat high blood pressure. Since the only variable that differs between the three groups is the type of ...
An experimental group in a scientific experiment is the group on which the experimental procedure is performed. The independent variable is changed for the group and the response or change in the dependent variable is recorded. In contrast, the group that does not receive the treatment or in which the independent variable is held constant is ...
In an experiment, the control is a standard or baseline group not exposed to the experimental treatment or manipulation.It serves as a comparison group to the experimental group, which does receive the treatment or manipulation. The control group helps to account for other variables that might influence the outcome, allowing researchers to attribute differences in results more confidently to ...
A control group in a scientific experiment is a group separated from the rest of the experiment, where the independent variable being tested cannot influence the results. This isolates the independent variable's effects on the experiment and can help rule out alternative explanations of the experimental results. Control groups can also be separated into two other types: positive or negative.
The alterations made to this group are deliberate and strategic, aiming to explore the effects of specific changes or treatments. Comparing the outcomes from the experimental group with those of the control group allows researchers to deduce the impact of the variable being tested, thereby, providing a framework for interpreting the results.
A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn't receive the experimental treatment. However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group's outcomes before and after a treatment (instead of ...
A true experiment (aka a controlled experiment) always includes at least one control group that doesn't receive the experimental treatment. However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group's outcomes before and after a treatment (instead of ...
In the design of experiments, hypotheses are applied to experimental units in a treatment group. [1] In comparative experiments, members of a control group receive a standard treatment, a placebo, or no treatment at all. [2] There may be more than one treatment group, more than one control group, or both. A placebo control group [3] [4] can be used to support a double-blind study, in which ...
In this experiment, the group of participants listening to no music while working out is the control group. They serve as a baseline with which to compare the performance of the other two groups. The other two groups in the experiment are the experimental groups. They each receive some level of the independent variable, which in this case is ...
Control groups that do not receive any treatment at all are called "passive control groups." A passive control group for the experimental reaction time training group would only take the pre- and the posttest without receiving any coordinated treatment (e.g., in form of playing video games in the laboratory) in between.
Conclusion. Experimental treatment studies function in the way that they involve different groups, one of which serves as a control group to provide a baseline for the estimation of the treatment effect. The treatment therefore defines the group as independent variable, which is manipulated and therefore makes the investigation an experiment.
By Jim Frost 3 Comments. A control group in an experiment does not receive the treatment. Instead, it serves as a comparison group for the treatments. Researchers compare the results of a treatment group to the control group to determine the effect size, also known as the treatment effect. A control group is important because it is a benchmark ...
Hugh Good. A control group is a common tool that researchers use. It allows them to prove a cause-and-effect relationship with an independent variable. This variable does not change for the control group. In this sense, the control group is the status quo. Researchers compare the effects in the experimental group against the control group.
What Is a Control Group in an Experiment. A control group is a set of subjects in an experiment who are not exposed to the independent variable. The purpose of a control group is to serve as a baseline for comparison. By having a group that is not exposed to the treatment, researchers can compare the results of the experimental group and determine whether the independent variable had an impact.
The archwires were divided into two groups: control group, not submitted to heat treatment and, thus, maintaining the as-received properties, and an experimental group, in which the archwires were submitted to heat treatment for distal bending at one end. Heat treatment was performed by a single experienced operator (J.O.A.).