Every A/B testing platform eventually forces a choice: does "significant" mean a p-value crossed 0.05, or does it mean a posterior probability crossed 95%? The two frameworks often agree on which variant wins, but they answer different questions, fail differently under peeking, and require different discipline when you're comparing your results against a StatFacts insight card. Getting this wrong doesn't just confuse a debrief — it changes how much you should trust the number you're about to ship on.

Two Questions That Sound Like One

The core confusion is linguistic before it's mathematical.

• Frequentist statistics ask: if there were truly no difference between variants, how surprising would data this extreme be? The p-value is that surprise, computed under a fixed null hypothesis, using a test statistic whose distribution is defined by imagining the experiment repeated infinitely.
• Bayesian AB testing asks: given the data I've observed and what I believed beforehand, how probable is it that B beats A, and by how much? The output is a posterior probability — a direct statement about the hypothesis, not about hypothetical repeated sampling.

A p-value of 0.03 does not mean "97% chance B is better." That's the single most common misreading in growth teams, and it's a category error: p-values describe the data's compatibility with the null, not the probability of any hypothesis being true. A Bayesian posterior probability of 97% for "B > A" is a direct answer to the question most stakeholders actually asked in the meeting.

Where Each Framework Breaks Down in Practice

Neither approach is safe by default — each has a specific failure mode that shows up in real dashboards.

Frequentist: the peeking problem

Classical significance testing assumes you fix your sample size (or stopping rule) in advance and look once. Checking the dashboard daily and stopping the moment p < 0.05 inflates the false-positive rate far above 5% — sometimes past 20-30% depending on how often you peek. This is why StatFacts' guide on preventing p-hacking treats early stopping as a primary risk, not an edge case. Sequential correction methods (alpha-spending, always-valid p-values) fix this, but plain t-tests and chi-square tests checked repeatedly do not.

Bayesian: the prior problem

Bayesian methods handle continuous monitoring more gracefully — the posterior updates honestly as data arrives and doesn't require a pre-committed stopping rule in the same way. But the framework shifts the risk into the prior. A strongly informative prior pulling toward "no effect" will understate a real lift; a flat, uninformative prior on a low-traffic test can make noisy early results look more decisive than they are. Teams that switch to Bayesian dashboards to escape peeking penalties sometimes just relocate the same optimism into prior selection, especially when the prior is quietly set to "whatever made last quarter's test look good."

Reading the Same Effect Two Ways

| Question | Frequentist answer | Bayesian answer | ||

Related Resources from StatFacts:

• For guidance on interpreting effect ranges, confidence labels, and sample context on any insight card: How to Read Benchmarks Effectively
• To check your own experiment's sample size and expected precision before choosing a framework: Benchmark Calculator

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