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Randomized Comparative Experiments. Do you believe this claim about strength shoes?. How might we design a study to investigate this claim?.
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Do you believe this claim about strength shoes? How might we design a study to investigate this claim?
The strength shoe is a modified athletic shoe with a 4-cm platform attached to the front half of the sole. Its manufacturers claim that this shoe increases a person’s jumping ability. If your friend who wears strength shoes can jump farther than another friend who wears ordinary shoes, would you consider that compelling evidence that strength shoes really do increase jumping ability? Explain. This is called anectodal evidence; it results from situations that come to mind easily or are convenient. Much of the practice of statistics involves designing studies and collecting data in such a way that we don’t have to rely on anecdotal evidence.
Now suppose that you take a random sample of individuals, you identify who does and does not wear strength shoes, and then compare their jumping ability (using some consistent way of measuring distance jumped). Identify the explanatory and response variables in this study, and classify each according to its type. Explanatory: Type: Response: Type: Suppose you find that the strength shoe group tends to jump much farther than the other group. Can you legitimately conclude that strength shoes caused the longer jump? Explain.
What’s the problem with this (observational) study? We cannot know whether the two groups might differ in more ways than just the explanatory variable—i.e., in other ways that might also explain the observed responses. Ex. Could subjects who chose to wear strength shoes be more athletic to begin with than those who opt to wear ordinary shoes? Ex. Could subjects who chose to wear strength shoes be taller than those who opt to wear strength shoes? What other potential confounding variables might be lurking?
When investigating whether one variable causes an effect on another variable, we need to create a comparison group and assign subjects to the explanatory group in a manner that tends to ensure the groups are virtually identical in all ways other than the explanatory variable. The above characterizes an experimental design study: the experimenter actively imposes the treatment (explanatory variable group) on the experimental units/subjects. A viable conclusion can then be reached about the explanatory variable’s direct effects on the response variable (later in the course we will learn to quantify the confidence in such conclusions)
A 1993 study published in the American Journal of Sports Medicine investigated the strength shoe claim with a group of 12 intercollegiate track and field participants (Cook et al., 1993). Suppose you also want to investigate this claim, and you recruit 12 of your friends to serve as subjects. You plan to have 6 friends wear strength shoes and the other 6 wear ordinary athletic shoes, and then measure their jump heights. How might you assign the subjects to these two groups in an effort to balance out potentially confounding variables?
Random assignment is the preferred method of assigning subjects to treatments (explanatory variable groups) in an experiment: this method gives each and every subject the same chance of being assigned to any of the treatment groups. Such a study is called a randomized comparative experiment. Describe in some detail how you might implement the process of randomly assigning your 12 subjects to treatments.
Watch Out! Random assignment is NOT the same thing as selecting a random sample from a population. Rather, it’s using a random process to determine which of the explanatory groups to assign subjects already chosen to make up a sample. Many (most) experimental studies (e.g., medical treatment studies with people) use volunteers from particular groups (age, gender, etc) that may not be representative of the potential population—not random samples of subjects chosen from that population. But experimental studies do use random assignment of subjects to treatment groups.
Comparison groups are especially important in medical studies because subjects often respond positively simply to being given a treatment, regardless of whether it is effective. This phenomenon is known as the placebo effect. Experimenters control for this effect by administering a placebo—an inert treatment, containing no active ingredient—to subjects in a control group. In this setup, subjects must be blind as to which treatment they actually receive or else that knowledge could affect their responses. Whenever possible, experiments should be double-blind—meaning that the experimenter must also be unaware of which subjects receive which treatment.
