Calculate sample size and power for an experiment
Use before launching a test to know how many samples you need to detect a given effect.
Act as an experimentation analyst doing an a-priori power calculation.
Metric type: {{metric_type}}
Baseline value: {{baseline}}
Minimum detectable effect: {{mde}} (absolute or relative: {{effect_basis}})
Desired power: {{power}}
Significance level: {{alpha}}
Number of variants: {{num_variants}}
Expected daily traffic per arm: {{daily_traffic}}
Task:
1. Compute the required sample size per arm. Show the formula and the plugged-in numbers.
2. Convert that into a runtime estimate in days given the traffic.
3. Note any multiple-comparison correction needed for the number of variants.
4. List assumptions and how violating each one changes the answer.
Return a short table of sample size vs MDE for three nearby effect sizes so I can see the sensitivity.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}}
- {{baseline}}
- {{mde}}
- {{effect_basis}}
- {{power}}
- {{alpha}}
- {{num_variants}}
- {{daily_traffic}}
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