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What is a problem with performing multiple hypothesis tests on the same data set?
Running multiple tests on the same data set at the same stage of an analysis increases the chance of obtaining at least one invalid result. Selecting the one “significant” result from a multiplicity of parallel tests poses a grave risk of an incorrect conclusion.
Can you have more than one hypothesis in research?
A single study may have one or many hypotheses. Usually, we call the hypothesis that you support (your prediction) the alternative hypothesis, and we call the hypothesis that describes the remaining possible outcomes the null hypothesis.
What is the multiple comparison test?
Multiple comparisons tests (MCTs) are performed several times on the mean of experimental conditions. When the null hypothesis is rejected in a validation, MCTs are performed when certain experimental conditions have a statistically significant mean difference or there is a specific aspect between the group means.
Do you need prior knowledge of multiple hypothesis testing?
If you want to read up on the above, the material is covered by any introductory statistics book, as well as many blog posts, e.g. [MT1] and [MT2]. On the other hand, no prior knowledge about multiple testing is necessary. Also, we ignore all of the intricacies that come with satisfying assumptions of tests.
What are the different types of hypothesis testing?
There are two types of tests: Multiple testing: for every hypothesis, we want to separately test each null hypothesis.
What is the critical value of hypothesis testing?
As good citizens, we gather our data after specifying our hypothesis and calculate the value of our test statistic Z and find Z = − 2.0 If you have ever taken a class about hypothesis testing, I’m afraid you know the critical value for such a two-sided test by heart: 1.96 telling you to reject the null hypothesis at the 5% significance level.
How is sample size used in hypothesis testing?
All the extra information we get from an increased sample size are used to improve power: increasing the probability of correctly rejecting the null hypothesis. reducing the probability of not rejecting the null hypothesis when we actually should. Hypothesis testing gets even more interesting when there are multiple hypotheses that we want to test.