Description
Part One – Multiple Testing
Read Lecture Seven. The lectures from last week and Lecture Seven discuss issues around using a single test versus multiple uses of the same tests to answer questions about mean equality between groups. This suggests that we need to master—or at least understand—a number of statistical tests. Why can’t we just master a single statistical test—such as the t-test—and use it in situations calling for mean equality decisions? (This should be started on Day 1.)
Part Two – ANOVA
Read Lecture Eight. Lecture Eight provides an ANOVA test showing that the mean salary for each job grade significantly differed. It then shows a technique to allow us to determine which pair or pairs of means actually differ. What other factors would you be interested in knowing if means differed by grade level? Why? Can you provide an ANOVA table showing these results? (Do not bother with which means differ.) How does this help answer our research question of equal pay for equal work? What kinds of results in your personal or professional lives could use the ANOVA test? Why? (This should be started on Day 3.)
Part Three – Effect Size
Read Lecture Nine. Lecture Nine introduces you to Effect size measure. There are two reasons we reject a null hypothesis. One is that the interaction of the variables causes significant differences to occur – our typical understanding of a rejected null hypothesis. The other is having a large sample size – virtually any difference can be made to appear significant if the sample is large enough. What is the Effect size measure? How does it help us decide what caused us reject the null hypothesis?
Your responses should be separated in the initial post, addressing each part individually, similar to what you see here.
Explanation & Answer
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Running head: STATISTICAL TESTS
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Statistics
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STATISTICAL TESTS
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Question One
Multiple statistical testing is important over single statistical testing because of the limit
of error. It adjusts the p-values multiple times, which is crucial in controlling the type I error rate.
Multiple testing increases the accuracy when conducting the analysis. The margin of error
decreases with the number of tests done. As such, the result that comes out when working the
mean equality decisions can be relied on. Simultaneous statistical testing makes the chances of
type 1 error exceed the nominal error. When using one statistical test in finding the mean
equality decision, it becomes difficult to make comparisons. On the other hand, multiple testing
results in some figures, which can be applied and used in the reduction of errors (Armstrong,
2014). The p-value adjustments are calculated with the different numbers of figures, which
single testing cannot provide.
In a way, it increased the chances of making t...
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