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Learn about the key components of a good experiment, including control groups, randomization, replication, and blinding. Understand the importance of comparing treatments, statistical significance, and generalizing results. Enhance your experimental design skills.
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Chapter 4Gathering Data Section 4.3 Good and Poor Ways to Experiment
Elements of an Experiment • Experimental units: The subjects of an experiment; the entities that we measure in an experiment. • Treatment: A specific experimental condition imposed on the subjects of the study; the treatments correspond to assigned values of the explanatory variable. • Explanatory variable: Defines the groups to be compared with respect to values on the response variable. • Response variable: The outcome measured on the subjects to reveal the effect of the treatment(s).
Experiments • An experiment deliberately imposes treatments on the • experimental units in order to observe their responses. • The goal of an experiment is to compare the effect of the treatment on the response. • Experiments that are randomized occur when the • subjects are randomly assigned to the treatments; • randomization helps to eliminate the effects of lurking • variables.
3 Components of a Good Experiment • Control/Comparison Group: allows the researcher to analyze the effectiveness of the primary treatment. • Randomization: eliminates possible researcher bias, balances the comparison groups on known as well as on lurking variables. • Replication: allows us to attribute observed effects to the treatments rather than ordinary variability.
Component 1: Control or Comparison Group • A placebo is a dummy treatment, i.e. sugar pill. Many • subjects respond favorably to any treatment, even a • placebo. • A controlgroup typically receives a placebo. A control • group allows us the analyze the effectiveness of the • primary treatment. • A control group need not receive a placebo. Clinical trials often compare a new treatment for a medical condition, not with a placebo, but with a treatment that is already on the market.
Component 1: Control or Comparison Group • Experiments should compare treatments rather than attempt to assess the effect of a single treatment in isolation. • Is the treatment group better, worse, or no different than the control group? • Example: 400 volunteers are asked to quit smoking and each start taking an antidepressant. In 1 year, how many have relapsed? Without a control group (individuals who are not on the antidepressant), it is not possible to gauge the effectiveness of the antidepressant.
Placebo Effect • Placebo effect (power of suggestion). The “placebo effect” is an improvement in health due not to any treatment but only to the patient’s belief that he or she will improve. • In some experiments, a control group may receive an existing treatment rather than a placebo.
Component 2: Randomization • To have confidence in our results we should randomly assign subjects to the treatments. In doing so, we • eliminate bias that may result from the researcher assigning the subjects. • balance the groups on variables known to affect the response. • balance the groups on lurking variables that may be unknown to the researcher.
Component 3: Replication • Replication is the process of assigning several experimental units to each treatment. • The difference due to ordinary variation is smaller with larger samples. • We have more confidence that the sample results reflect a true difference due to treatments when the sample size is large. • Since it is always possible that the observed effects were due to chance alone, replicating the experiment also builds confidence in our conclusions.
Blinding the Experiment • Ideally, subjects are unaware, or blind, to the treatment • they are receiving. • If an experiment is conducted in such a way that neither • the subjects nor the investigators working with them • know which treatment each subject is receiving, then • the experiment is double-blinded. • A double-blinded experiment controls response bias • from the subject and experimenter.
Define Statistical Significance • If an experiment (or other study) finds a difference in two (or more) groups, is this difference really important? • If the observed difference is larger than what would be expected just by chance, then it is labeled statistically significant. • Rather than relying solely on the label of statistical significance, also look at the actual results to determine if they are practically significant.
Generalizing Results • Recall that the goal of experimentation is to analyze the association between the treatment and the response for the population, not just the sample. • However, care should be taken to generalize the results of a study only to the population that is represented by the study.
SUMMARY: Key Parts of a Good Experiment • A good experiment has a control comparison group, randomization in assigning experimental units to treatments, blinding, and replication. The experimental units are the subjects—the people, animals, or other objects to which the treatments are applied. • The treatments are the experimental conditions imposed on the experimental units. One of these may be a control (for instance, either a placebo or an existing treatment) that provides a basis for determining if a particular treatment is effective. The treatments correspond to values of an explanatory variable.
SUMMARY: Key Parts of a Good Experiment • Randomize in assigning the experimental units to the • treatments. This tends to balance the comparison groups • with respect to lurking variables. • Replicating the treatments on many experimental units • helps, so that observed effects are not due to ordinary • variability but instead are due to the treatment. Repeat • studies to increase confidence in the conclusions.
