Properties2
| Type | Concept |
| Note created | Feb 17, 2025 |
Fisher’s method is a technique for “meta-analysis” developed by Ronald Fisher that it’s used to combine the results of several independence tests bearing upon the same hypothesis. The main essence of the Fisher’s method is combining p-value probabilities into a single test statistic.
Combining weak tests
The main use of it is to combine several potentially-weak tests and combining them into a stronger one – as explained in this example:
Imagine four insecticide experiments:
- two t-tests, P = 0.11 and P = 0.12
- a G-test, P = 0.21
- a regression, P = 0.08
Nothing significant, right? Wrong. Fisher’s method gives a test statistic of 16.8, with 8 degrees of freedom and a combined P = 0.03. This shouldn’t shock you: while none of the individual tests have P below the (absolutist) threshold of 0.05, it’s unlikely that four experiments would get four smallish values in the absence of any real effect.
There is one important caveat though: finding a significant result in Fisher’s method means that you have found an existing pattern, but you have not measured its effect.