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Section 4.3 Good and Poor Ways to Experiment

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Section 4.3 Good and Poor Ways to Experiment Chapter 4 Gathering Data Section 4.3 Good and Poor Ways to Experiment Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 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). 3 Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 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. 4 Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 3 Components of a Good Experiment 1. Control/Comparison Group: allows the researcher to analyze the effectiveness of the primary treatment. 2. Randomization: eliminates possible researcher bias, balances the comparison groups on known as well as on lurking variables. 3. Replication: allows us to attribute observed effects to the treatments rather than ordinary variability. 5 Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 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 control group 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. 6 Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 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. 7 Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 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. 8 Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 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. 9 Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 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. 10 Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 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. 11 Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 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. 12 Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 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. 13 Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 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. 14 Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 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. 15 Copyright © 2013, 2009, and 2007, Pearson Education, Inc..
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