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Chapter 11 notes - Chapter 11 Analyzing Complex Designs...

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Chapter 11 Analyzing Complex Designs
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Overview – Example Review of Chapter 9 Experiments typically involve more than two levels of the IV Example: Weight loss 53 obese individuals randomized to 5 conditions Reward Punishment Both reward and punishment Monitor with neither reward nor punishment No monitoring (control group)
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Overview – Example To analyze this data we could run 10 t- tests Means for group 1 vs group 2 Means for group 1 vs group 3 Etc... There is a problem here
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The Problem of Multiple Tests Remember that the alpha (.05) level relates to the risk of making a Type I Error The alpha level refers to a single test Type I error increases as we perform a greater number of tests If our alpha level was set at .05 and we conducted 100 different t-tests we would erroneously see reject the null hypothesis for 5 tests With t-tests the more we conduct, the more likely we are to make a Type I error
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The Problem of Multiple Tests The probability of making a Type I error is 1 – (1 – alpha) c C = the number of tests Weight loss example 1- (1- .05) 10 = .40
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Preventing Type I Error You can prevent Type I error inflation by using the Bonferoni adjustment: divides
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