Determining sample size requirements

Sample size defines the number of users needed in each test variation and directly impacts the reliability of your A/B test results. Getting this number right is crucial for meaningful outcomes.

Four key factors determine your required sample size:

• Expected effect size: The magnitude of the difference between variations. Smaller differences need larger samples because they're harder to distinguish from random fluctuations.
• Baseline conversion rate: Your current performance level. Lower baseline rates require larger samples to detect meaningful changes.
• Statistical confidence level: Typically 95%, indicating you're 95% confident your results aren't due to chance.
• Statistical power: Usually 80%, representing the probability of detecting a true effect when it exists.

When planning your sample size:

• Use a sample size calculator as a starting point
• Assess if your traffic volume can support the recommended sample size within a reasonable timeframe
• Define what minimum improvement justifies the resources required for testing
• Consider whether seasonal patterns might affect your results if the test duration is too short

The right sample size balances statistical validity with practical testing constraints. Too small a sample risks unreliable conclusions, while unnecessarily large samples waste time and resources. If your organization has analysts or data scientists, consider involving them early. They can help you determine the appropriate sample size and estimate how long the test needs to run.