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The perils of multiple statistical tests: controlling for false positives.
David Sidebotham1, Xiaoyu Chen2
1Cardiothoracic and Vascular Intensive Care Unit, Auckland City Hospital, Auckland, New Zealand; Department of Anaesthesiology, Faculty of Health Science, University of Auckland, Auckland, New Zealand.
Statistical testing has errors like false positives. Controlling for multiple tests is crucial, as the risk of false positives increases significantly with more tests, impacting research reliability.
Area of Science:
- Statistics
- Biostatistics
- Research Methodology
Background:
- Statistical testing inherently produces errors, including false positives (Type I errors) and false negatives (Type II errors).
- False positives are commonly managed using familywise error rate (FWER) or false discovery rate (FDR) control.
- A 0.05 significance threshold implies a 5% chance of a false positive per test, but this risk escalates substantially across multiple tests.
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