Experiment statistics overview - Docs - PostHog
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A working understanding of statistical methodology is helpful to feel confident about interpreting experiment results. For those without prior…
Bayesian statistics Contents Input data: What goes into the analysis Funnel metrics Mean metrics Ratio metrics What the experimentation pipeline does Step 1: Data aggregation Aggregation into sufficient statistics Outlier handling (winsorization) Step 2: Data quality validation Step 3: Calculate effect size and variance Effect size calculation Variance calculation Step 4: Bayesian posterior update The prior distribution The posterior distribution Step 5: Generate results Chance to win Credible interval Significance (decisiveness) Configuring the confidence level Mathematical formulas reference
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