Run a Bayesian interpretation of an A/B test
Use when you want probability-of-being-best instead of a frequentist p-value.
Act as a Bayesian experimentation analyst.
Metric type: {{metric_type}}
Control data: {{control_data}}
Variant data: {{variant_data}}
Prior beliefs (or use weak priors): {{prior}}
Task:
1. Choose an appropriate likelihood and conjugate prior for this metric type and state them.
2. Derive or describe the posterior for each arm.
3. Compute the probability that the variant beats control and the expected loss of choosing each arm.
4. Give the Python (numpy or PyMC) to simulate the posteriors and these quantities.
5. Give a plain-language decision recommendation and when to stop the test.
Contrast this briefly with what a frequentist p-value would and would not tell me.Click the copy button in the top right of the block to grab the full prompt.
Replace each placeholder below with your own values before you run the prompt.
- {{metric_type}}
- {{control_data}}
- {{variant_data}}
- {{prior}}
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