Statistical Learning: 13.2 Introduction to Multiple Testing and Family Wise Error Rate | Summary and Q&A

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October 7, 2022
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Statistical Learning: 13.2 Introduction to Multiple Testing and Family Wise Error Rate

TL;DR

Testing multiple hypotheses can lead to an increased chance of making Type I errors, which can be problematic in situations with a large number of tests.

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Questions & Answers

Q: Why does testing multiple hypotheses become more complicated with a large number of tests?

When there are thousands or tens of thousands of hypothesis tests, the chances of making Type I errors increase significantly. The methods used for adjusting in situations with a small number of tests may not work effectively in these cases.

Q: How do p-values play a role in determining whether to reject a null hypothesis?

If the p-value falls below a certain threshold, typically set at 1 percent, the null hypothesis is rejected. However, when there are multiple tests, this can lead to a higher chance of false positives.

Q: What is the significance of the thought experiment involving flipping coins?

The thought experiment demonstrates the concept of p-values and Type I errors. Even with 1024 fair coins, flipping each coin 10 times would result in at least one coin with a p-value below 0.002, leading to a false rejection of the null hypothesis.

Q: How does the concept of multiple testing relate to reproducibility issues?

Performing a large number of hypothesis tests increases the likelihood of finding false positives. This can lead to exaggerated claims and difficulties in reproducing the results, contributing to reproducibility issues.

Summary & Key Takeaways

  • Testing multiple hypothesis tests becomes more challenging when dealing with a large number of tests.

  • Rejecting all null hypotheses with p-values below a certain threshold can result in a significant number of Type I errors.

  • Performing a large number of hypothesis tests increases the likelihood of false positives, even with a small p-value cutoff.

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