Multiple comparisons, multiplicity or multiple testing problem occurs when many statistical tests are performed on the same dataset. Each test has its own chance of a Type I error (false positive), so …

A typical microarray experiment might result in performing 10000 separate hypothesis tests. If we use a standard p-value cut-off of 0.05, we’d expect 500 genes to be deemed “significant” by chance.

If you run a hypothesis test, there’s a small chance (usually about 5%) that you’ll get a bogus significant result. If you run thousands of tests, then the number of false alarms increases dramatically.

Mar 21, 2016 · Multiple testing refers to situations where a dataset is subjected to statistical testing multiple times - either at multiple time-points or through multiple subgroups or for multiple end …

Multiple testing refers to any instance that involves the simultaneous testing of several hypotheses. This scenario is quite common in much empirical research in economics.

Dec 13, 2021 · Common examples of multiple testing problems include testing whether several variables have an effect on a given outcome, or testing the effect of a single variable on a myriad of …

This chapter discusses some approaches to correcting our inference methods when we are doing multiple tests.

With 100 tests of true nulls, the chance of making at least one false rejection is virtual certainty. That is the essence of the multiple testing problem: how do you control error rates when you do lots of tests?

For multiple testing problems there are several methods to control the family-wise error rate (FWER). FDR controlling procedures are promising alternatives to more conservative FWER controlling …

This article describes what multiple testing is and illustrates the problem with multiple testing using clinical examples. The authors identify and provide examples of common sources of multiple …