Explain multiple testing correction with an example

Question

Explain multiple testing correction in simple terms and provide a concrete example.

Codemia · Accepted Answer

Start by formalizing the problem:

1. **Define the hypotheses**: H0 (null hypothesis) and H1 (alternative). Be precise about what you're testing.

2. **Choose the test**: t-test for comparing means, chi-squared for categorical data, ANOVA for multiple groups, Mann-Whitney for non-normal distributions.

3. **Set the significance level**: Typically alpha = 0.05. Justify if you need a different threshold (e.g., multiple comparisons).

**Compute the test statistic**: Apply the appropriate formula using the sample data.

**Find the p-value**: The probability of observing a result this extreme (or more extreme) under the null hypothesis.

**Make a decision**: If p-value < alpha, reject H0. But always consider: statistical significance does not imply practical significance. A 0.1% improvement may be statistically significant with a large enough sample but meaningless in practice.

**Multiple testing**: If you run 20 tests at alpha=0.05, you expect 1 false positive. Apply Bonferroni or FDR correction.

**Simpson's Paradox**: A trend that appears in aggregated data can reverse when segmented. Always check subgroups.

**Survivorship Bias**: You only see users who stayed. Those who churned might tell a different story.

**Confounding Variables**: Correlation does not imply causation. Use randomization (A/B tests) or causal inference methods to establish causal relationships.

Explain multiple testing correction with an example

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