Post Hoc tests > Familywise Error Rate
What is the Familywise Error Rate?
The familywise error rate (FWE or FWER) is the probability of a coming to at least one false conclusion in a series of hypothesis tests . In other words, it’s the probability of making at least one Type I Error. The term “familywise” error rate comes from family of tests, which is the technical definition for a series of tests on data.
The FWER is also called alpha inflation or cumulative Type I error.
The formula to estimate the familywise error rate is:
FWE ≤ 1 – (1 – αIT)c
- αIT = alpha level for an individual test (e.g. .05),
- c = Number of comparisons.
For example, with an alpha level of 5% and a series of ten tests, the FWER is:
FWE = ≤ 1 – (1 – .05)10 = .401.
This means that the probability of a type I error is just over 40%, which is very high considering only ten tests were performed.
Controlling the FWERYou need to control the FWER for one main reason: If you run enough hypothesis tests (dozens, hundreds, or sometimes tens of thousands) you’re highly likely to get at least one significant result — a “false alarm” where you incorrectly reject the null hypothesis.
Two main procedures are used to control the FWER: single step and sequential.
- Divide the alpha level by the number of tests you’re running and apply that alpha level to each individual test. For example, if your overall alpha level is .05 and you are running 5 tests, then each test will have an alpha level of .05/5 = .01.
- Apply the new alpha level to each test for finding p-values. In this example, the p-value would have to be .01 or less for statistical significance.
Similar to Bonferroni, but makes adaptive adjustments to each p-value. Several sequential methods exist. The easiest is probably the Holm-Bonferroni Method, but several others have been developed including the Sidak-Bonferroni and Holland-Copenhaver.
- Holm-Bonferroni: tests are run and then ordered from lowest to highest p-values. The individual tests are then tested (starting with the one with the lowest p-value) with an overall Bonferroni correction for all tests. See: Holm-Bonferroni Method for a step-by-step example.
- Sidak-Bonferroni (sometimes called the Boole or Dunn approximation): a variant of Bonferroni which uses a Taylor expansion (from calculus).
Olejnik,S., Li, J., Supattathum, S., and Huberty, C.J. (1997). Multiple testing and statistical power with modified Bonferroni procedures. Journal
of educational and behavioral statistics, 22, 389-406.
Holland, B. S., and M. D. Copenhaver. 1987. An improved sequentially rejective Bonferroni test procedure. Biometrics 43: 417–423.
If you prefer an online interactive environment to learn R and statistics, this free R Tutorial by Datacamp is a great way to get started. If you're are somewhat comfortable with R and are interested in going deeper into Statistics, try this Statistics with R track.Comments? Need to post a correction? Please post on our Facebook page.