Multiple Comparisons
Conducting multiple independent-samples -tests to compare every pair of group means in an experiment creates a statistical problem. While a single -test has a chance of mistakenly rejecting a true null hypothesis (a Type I error), conducting several -tests simultaneously causes this risk to compound. As more tests are run, the overall probability of mistakenly rejecting at least one true null hypothesis becomes unacceptably high, necessitating specialized statistical procedures to control the error rate.
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Research Methods in Psychology - 4th American Edition @ KPU
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Multiple Comparisons
Studentized Range Distribution
Multiple Comparisons
Confidence Region
Degrees of Freedom (Independent-Samples t-Test)
Multiple Comparisons
Null and Alternative Hypotheses for the Independent-Samples t-Test
Formula for the Independent-Samples t-Test
Example of an Independent-Samples t-Test
What is the primary purpose of conducting an independent-samples t-test?
Arrange the steps in the correct logical sequence for conducting a statistical analysis to compare the average scores of two distinct groups of participants.
Multiple Comparisons
In statistical analysis, what is the primary purpose of conducting post hoc comparisons after an initial test rejects the null hypothesis?
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Modified t-Test Procedures
What is the primary statistical problem that arises when conducting multiple independent-samples t-tests to compare every pair of group means in an experiment?
A researcher conducts six separate t-tests to compare every pair of means among four experimental groups, using an alpha level of .05 for each test. Because each individual test maintains only a 5% chance of a Type I error, the overall probability of making at least one Type I error across all six tests also remains at 5%.