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Practical guide to bayesian vs frequentist ab testing: formulas, workflow, implementation pitfalls, and a direct execution playbook with A/B Test Calculator.
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Statistical significance (Z-test) and confidence intervals.
You ran an A/B test. Control: 1,000 users, 50 conversions (5.0%). Variant: 1,000 users, 65 conversions (6.5%). Is variant B better? The answer depends on which framework you use.
Frequentist testing asks: "If there were no real difference, how often would we see data this extreme?"
For the example above, a two-proportion z-test gives:
The output is binary: significant or not. The p-value is NOT the probability that B is better — it is the probability of seeing this data (or more extreme) assuming B is identical to A.
Bayesian testing asks: "Given the data, what is the probability that B is better than A?"
Using a Beta(1,1) prior (uninformative), the posterior distributions are:
Monte Carlo simulation (or closed-form calculation) gives: P(B > A) = 93.8%.
The output is continuous: "There is a 93.8% chance variant B has a higher conversion rate." You can also compute: "Expected lift is +1.4 pp, with 90% credible interval [-0.1 pp, +3.0 pp]."
Frequentist is better when:
Bayesian is better when:
With the same data above:
Neither answer is "wrong" — they answer different questions. Use A/B Test Calculator to compute both perspectives on your data.
Enter your control and variant data into A/B Test Calculator and compare the frequentist p-value with the Bayesian probability of improvement.
This article is reviewed by the Tools Hub editorial team for factual accuracy, practical relevance, and consistency with current product workflows.
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