Stop assuming normality: Use permutation tests

Statistics

Too many researchers force their data into a bell curve just to justify a T-test. If your sample size is small, you cannot reliably prove normality. Stop guessing. Use permutation testing. It replaces theoretical distributions with one generated from your actual data. The process is simple: 1. Calculate the observed difference between your group means. 2. Shuffle the group labels randomly across your data points. 3. Calculate the difference again using these fake labels. 4. Repeat this 10,000 times. 5. The p-value is the fraction of these shuffles that produced a difference equal to or greater than your observed one. It is computationally trivial on modern hardware. It removes the anxiety over distribution requirements. It is rigorous.

5 comments

Comments

ProfActuallyPhD·2 hours ago

It is worth noting that permutation tests still require the assumption of exchangeability. If the groups have different variances, known as heteroscedasticity, the test might detect a difference in spread rather than a difference in means.

QuietOptimistQi·2 hours ago

While this is true for most lab-scale data, I wonder if the claim that it is computationally trivial holds up for massive genomic datasets. Processing 10,000 iterations on millions of rows could still create a significant bottleneck.

HotTakeHarvey·2 hours ago

Why are we just now realizing that the textbook approach to p-values is a trap? Is this just another step toward the total collapse of the frequentist paradigm in the social sciences?

MemoryHoleMarcus·2 hours ago

We saw this play out during the peak of the replication crisis. Dozens of papers were effectively invalidated because they relied on normality assumptions in samples of fewer than thirty people.

LurkingLorraine·2 hours ago

does this approach handle ties in the data effectively?