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š§µ Why the obsession with p < 0.05 is hurting science. A meme. A truth. A reality check.
1/ This meme says it all. p = 0.0501? Pain. p = 0.0499? Pure euphoria. Two numbers. Nearly identical. Yet we treat them like night and day. Why?
2/ The 0.05 p-value threshold is arbitrary. It came from R.A. Fisher in the 1920s. And weāve been worshipping it like a sacred line ever since. But itās not magic. It's convention.
3/ What does p < 0.05 actually mean? It means: If the null hypothesis is true, thereās a 5% chance weād see this extreme of a result by random chance. Thatās it. Not: "This is true." Not: "This will replicate."
4/ p = 0.0499 and p = 0.0501 are nearly identical. But one gets you a āsignificantā label. The other gets dismissed. Thatās broken thinking.
5/ Quoting Mike Love: āA smaller p-value is not more interesting.ā āWe should focus on effect sizes.ā Heās right.
6/ Whatās an effect size? It tells you how big the difference is. Not just if itās statistically detectable. A gene with a log2 fold change of 3 matters. Even if p = 0.06.
7/ P-values shrink with more data. Got 10,000 samples? Youāll find āsignificanceā for even the tiniest differences. Statistically significant ā Biologically meaningful.
8/ Also, be careful when testing thousands of genes. Even with a p < 0.05 threshold, false positives will sneak in. Use multiple testing correction: FDR, Bonferroni. Always.
9/ Letās reframe: Instead of: āDid I beat the p < 0.05 line?ā Ask: Is the effect meaningful? Is it reproducible? Does it make biological sense?
10/ Want a better practice? Look at the distribution of p-values. Report adjusted p-values (FDR). Highlight effect sizes. Donāt cherry-pick.
11/ And donāt forget confidence intervals. They show the range of plausible effect sizesānot just a binary yes/no. More context, more truth.
12/ Key takeaways: 0.05 is a line in sand, not a cliff p-values ā effect size Focus on biological meaning Always correct for multiple testing Use p-values as part of the storyānot the whole story
13/ If you're making big decisions based on p = 0.0499 vs 0.0501... You're not doing science. You're doing stats theater. Look deeper. Think harder. Go beyond the stars.
14/ And pleaseāshare this with a friend still chasing tiny p-values. Letās stop celebrating noise and start celebrating insight.
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