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Explanation of Key Concepts in The Classic Experiment

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In science, as in life, the word experiment can have different meanings. If you put a lion and a tiger in the same cage just to see what happens, you could call it an experiment. In a research vein, such experiments would be termed exploratory, since you are exploring an unknown. In this paper however, I am going to discuss the kind of experiment that evaluates a causal link, an experiment that is designed to find out if one thing does really cause another something to happen. You could call these inferential experiments since they are used to make inferences about causal relationships. I am going to discuss the most simple form of an such an experiment, so I will call it a simple or classic experiment.

Inferential experiments are considered the most important method for establishing evidence or proof of causation. This is because the inferential experiment is really a situation a scientist creates that simulates the conditions under which the causal influence is supposed to occur. In the experimental situation, a causal effect is allowed to occur or to not occur. If it does occur, it is evidence that the causal link really exists. If it does not, the seeming causal link may not be real.

In an inferential experiment, the experimenter makes a formal statement of the causal relationship that the experiment is designed to prove or to disprove. That statement is call the hypothesis.

For example, let's say we wanted to find out if a particular antibiotic will kill a particular germ in an infected patient. We could find a hospital that has patients that have the infection, and give them the antibiotic. Then either the antibiotic causes the germs to die and the infection gets better, or it does not, right? Consider this however ... what if the germs die in the patients that got the pill, but in that hospital it also dies in the patients that didn't get the pill? You see, we have to look at the other side of the coin. We could have spend millions making an antibiotic that didn't really work when really it was something else in the hospital that killed the germ.

Now, let's say we want to find out if a certain drug can cause depression patients to become happier, more active, and less depressed. Well, we could round up a bunch of depression patients and give them the drug, right? (The people we are experimenting on are called the subjects or the participants.) If they become more active how will we know, if the drug really worked or if they just saw a bunch of other people there and started a party. To make sure we would have to conduct the experiment under the right conditions.

The basic idea of an experiment is to treat two groups of participants exactly the same
in every detail except one. That one factor is the experimental treatment that we give only to the experimental group. Later we examine the two groups to see if the experimental treatment made a difference between them. If there is a difference then the cause must have been the experimental treatment.

For example, lets say that we get depression patients together again, but we only give some of them the pill. The ones who get the pill are in the experimental group because they are the ones we are REALLY experimenting on - heh heh! After giving the experimental group the pill we later notice that the ones who got the pill started partying while the other one's seem to stay depressed. We call the other ones the control group because they provide a basis for controlling the influence of other unknown factors in the experimental setting we may not know about, like the other factors in the hospital that killed the germs in the first experiment I talked about above. The overall treatment give to the control group is the control treatment.

Meanwhile back at the lab ... your lab assistant is in charge picking patients for the experimental group and the control group. He notices that about half the patients are dressed as clowns (a home remedy for depression) so he tells the patients dressed as clowns to get in the experimental group line for one pill and the ones not dressed as clowns should get in the other (control group) line. Do you see a problem here?

This is a kind of experimenter bias. You are selecting one group based on some criteria (being dressed as a clown) that could reflect a difference in their depression level (maybe the people dressed as clowns are more cheerful, since perhaps its hard to feel depressed while wearing a big red nose, or maybe they are more depressed, after all who likes to be referred to as that clown.) Anyway by selecting this way you may introduce a difference between the group that is not due to the medication. After all, you want to know if the medication makes a difference, not the big red noses.

How do you select people for each group then? Flip a coin? YES, well perhaps you would not literally flip a coin (you would be more likely to use a computer program) but you would assign participants to the groups randomly. Any participant could be assigned to either group, which group he or she ends up in is literally just the luck of the draw.

Why random? Because random it is completely unbiased, you can't influence who gets in what group so you can't accidentally introduce some difference between the groups. This is how it is done in a proper experiment.

OK, so lets say you assign the participants to each group randomly, and you give the experimental group the pill. Well, if the experimental group starts partying, and the control group doesn't, it must mean the pill worked, right? Wait a minute, what if the experimental group started partying just because they were the lucky ones who got the pill, and the control group stayed depressed because they didn't get anything? Hey, this kind of thing used to happen so often that there is a special name for it, its called the placebo effect. The placebo effect is an improvement that happens just because the participant got some kind of something, a pill or some other kind of treatment. This placebo effect can mess up the results of your experiment by making you think that the medical action of your pill made the people better when really it was just the fact that they think somebody or some thing is helping them.

The way this effect, the placebo effect, is usually controlled is by giving both the experimental group and the control group something. For example, where the treatment is a pill, you would give the experimental group the medication and the control group a harmless sugar pill made to look like the medication. That way both groups get the feeling that they are getting something. When you give the control group their something, that something is called a placebo. OK, so in an experiment like this everybody has to get something so that the placebo effect is the same in both the experimental and control groups. Again, we want the experimental and control groups treated exactly the same, except for that one thing ...


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Now that we have discussed all this placebo stuff, let's say that you do the experiment again, only this time the experimental group gets the experimental treatment and the control group gets a placebo. Then you notice one of your lab assistants over with a group of patients telling jokes and slapping patients on the back. Then you notice in the record that these are the patients in the experimental group. YIKES!! What if your lab assistants start cheering up the experimental group and ignoring the control group, could that mess up the results??

This sort of conduct on the part of the researchers in an experiment is also form of experimenter bias. If you call the research assistant over and you say "don't you see you are messing up the experiment, it's the pill that is supposed to cheer up the patients, not the experimenters." But your assistant says, "Gee doc, I was just cutting up a little bit, you know, ... I'm happy for them getting better and all and besides, they all knew that they were the one who got the REAL pills by the way Lois was grinning at them when she gave them the pills." "Lois just looked guilty when she handed out the fake pills, oh, you know those placebo pills, I think she just felt sorry for them ... heck I don't blame her and ... (blah blah blah)." Do you see a problem here?

Yeah, this kind of thing does bias the results of experiments because, as I said before, its the medication, and not the experimenters, that's supposed to make the difference. There is a special procedure for preventing this kind of bias. It is called the double blind procedure.

Why is it called double blind and not just plain blind? Its called double blind because there are two groups who are blind to who gets what pill. These two groups are the participants themselves, and (guess who else) ... those kind and friendly but rascally lab assistants. That's right! If the lab assistants (or any other member of the research team who has contact with the patients) knows who got what pill, and they could start treating the participants differently, that difference in treatment could account for final differences between the two groups.

During the double blind procedure, there is usually just one researcher who puts the pills in bottles and other researchers (who don't know which pill is in which bottle) actually give the pills to the participants. In an experiment like this there is usually a special list of questions that is used by a researcher to measure just HOW depressed each participant is. A member of the research team fills in the answers while interviewing the participant, and records the results. You especially want to make sure that the researcher who administers any such measurement does NOT know whether the participant he or she is interviewing in is the experimental group or the control group. If they did know, it could bias the way they perceive and react to the participant, thereby resulting in biased results.

Now let me explain what is meant by the terms independent variable and dependent variable. As you already know, a variable is a concept that can take on more than one value (in other word it can vary). For example, depression is a concept that can take on the values of extreme, severe, moderate, mild, none, and perhaps elated could be another value. Another possible variable, for example is intelligence, which could be below average, average, above average, etc.

In the experiment we talked about above you have one variable ( like a pill) that is supposed to exert a causal influence on another variable, depression. Well the variable that is supposed to be causally influenced is called the dependent variable, because (using the experiment we discussed above as an example) how depressed you are is supposed to depend on whether you got the medication.

The independent variable is the something that is supposed to exert the causal influence. Again, using the experiment we discussed above as an example, the independent variable is the medication. You might say, if it's the medication what are the values it can take on? In the experiment above it take on the values of present, as in the real pill, or not present, as in the placebo. Thus you could say that the independent variable, namely the presence or absence of the medication, is supposed to exert a causal influence of reducing the level of the dependent variable, depression.

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Students ask me, "why call it the independent variable ... what's it supposed to be independent of?" No one has an exact historical explanation. For each participant, the value of the dependent variable (how depressed they are) is finally supposed to depend on the value of the independent variable he or she got (medication present or not present). But whether or not a particular participant got the medication must NOT depend on how depressed he or she was. In fact, you are specifically not supposed to let someone's level on the dependent variable influence what they get on the independent variable.

OK, here are the last concepts in experimentation that I need to mention. In the experiment discussed above, the difference made by the independent variable is the difference between the two groups. You can tell if the independent variable made a difference or not by looking at the difference between the two group on the dependent variable. This is called a between groups experiment.

What if we couldn't get enough participants? Well we could try giving the pills to all the participants, but at different times. Let's say in the first week of the experiment we give some of them the real pill and some the placebo, and the second week we reverse it so the ones who got the real pill the first week get the placebo the second and the one who got the placebo the first week get the real pill the second. We would want to see if there is a difference in each individual's depression when he or she was getting the real pill versus when he or she was getting the placebo. The difference we are looking for here is not the difference between two different groups, but the difference within the participants at different times. This is called a within group or within groups experiment. In a within groups experiment there is no separate control and experimental groups, but the terms experimental treatment and control treatment are still used.

When I started this discussion I said that I was going to discuss a simple or classic inferential experiment. Such an experiment has just one control group and just one experimental group, but in real experimentation you may have numerous experimental and control groups. For example, you could have one control group who gets no placebo, one who gets a placebo, and another one who gets an old treatment for depression (if you are trying to prove a new treatment is better). You could have an experimental group who gets the pill, another who gets psycho-therapy instead, and a third experimental group that gets both pill and psychotherapy.

The reason I also called the simple experiment classic is because it includes all the basic terms you need to understand in order to get a good grounding in what experimentation is about. I also mentioned the within groups experiment because it is one of the most common types of experiments. Within groups experiments are appropriate for a lot of different situations, they use is not limited to situations where "you don't have enough participants".

Lastly, you need to remember that all inferential experiments do not involve pills or drugs. You could, for example, experiment with the effectiveness of training the participants a learning strategy, and see if those who are taught the strategy (the independent variable) learn to perform a particular task (dependent variable) better than another (control) group who didn't get the learning strategy. The possibilities for different kinds of experiments are endless. By understanding the concepts in experimentation presented here, you will be better able to evaluate and understand the thinking that goes into planning and evaluating an experiment.

Here are some key terms again:

independent variable - in an experiment the factor that which is suppose to make "it" happen, the causative factor.

dependent variable - the variable that is supposed to be affected or changed by the independent variable, so its value depends on the value of the independent varialbe.

hypothesis - a statement of what the experiment is supposed to prove.
Example: Aspirin helps to relieve common headaches.
Example: Training in temper control can help people with anger control problems to be more successful on the job.

control group - in an experiment, the group that does not get the experimental treatment, they provide an "untreated" basis of comparison for the experimental group

experimental group -- in an experiment, the group that does get the experimental treatment. If the independent variable had its supposed effect, that effect will be reflected as the difference between the control group and experimental group

primary research - research in which data is actually collected from the natural world (including experiments, naturalistic observation, case studies, etc.). This contrasts with secondary research that draws information from books or publications or expert opinion.

"between groups" vs. "within groups" design - in an experiment, a "between groups" design has at least one experimental group and one control group. The effect of the independent variable (the experimental treatment) is measured by examining the difference between the control group and the experimental group on the dependent variable. Thus the effect of the experimental treatment measured as the difference between the groups on the dependent variable. In a "within groups" design, the same group receives the control treatment at one point in time and the experimental treatment a another point in time. The effect of the independent variable is measured by examining the difference in the dependent variable at the time of the the control treatment and the dependent variable at the time of the experimental treatment.

placebo - in an experiment, the control treatment in which the control group is treated in a harmless but unhelpful way, without know that the treatment is really not meant to elicit a change in the dependent variable. An example placebo is a sugar pill which resembles the real medication used in the experiment.

experimenter bias - any effect that the behavior of the experimenter has on the experiment that leads to erroneous results.

double blind procedure - a procedure used in an experiment where some of the experimental team and all of the participants are not informed as to who is in the experimental group and who is in the control group. For the experimental team, those who actually administer a drug and those who take measurements on the participant are especially kept in the dark concerning who is in which group.

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The content of this page is Copyright 2000, 2001, 2012 Bernard Schuster. The term WebQuest, and template design are Bernie Dodge's. Teachers can print copies of the pages to use as teaching aids for their classes, email me for permission before installing electronic copies.

This page provides explanations of common terms used in experiments. The intent is help students to develop their understanding of the basic concepts in experimentation. Ad Choices content is selected by Google.