[murphyslab]

A Bonferroni correction would be suitable. I usually see it used in genome-wide association studies (GWAS) that check to see if a trait or phenotype is influenced by any single nucleotide polymorphisms (SNPs) in a genome. So it's doing multiple testing on a scale of ~1 million.

> One of the simplest approaches to correct for multiple testing is the Bonferroni correction. The Bonferroni correction adjusts the alpha value from α = 0.05 to α = (0.05/k) where k is the number of statistical tests conducted. For a typical GWAS using 500,000 SNPs, statistical significance of a SNP association would be set at 1e-7. This correction is the most conservative, as it assumes that each association test of the 500,000 is independent of all other tests – an assumption that is generally untrue due to linkage disequilibrium among GWAS markers.

https://journals.plos.org/ploscompbiol/article?id=10.1371/jo...

cf: https://en.wikipedia.org/wiki/Bonferroni_correction