Fixed Sample Testing

Fixed Sample Testing is a statistical testing methodology where the sample size is predetermined before data collection begins, based on factors like desired power, significance level, and effect size. It is commonly used in hypothesis testing, such as A/B testing in software development, to ensure reliable and statistically valid results. This approach contrasts with sequential testing methods where sample size can be adjusted during the experiment.

Also known as: Fixed Sample Size Testing, Fixed N Testing, Pre-determined Sample Testing, Static Sample Testing, Non-sequential Testing

Why learn Fixed Sample Testing?

Developers should use Fixed Sample Testing when conducting controlled experiments, like A/B tests for feature rollouts or performance optimizations, to avoid biases from early stopping and ensure results meet predefined statistical standards. It is particularly valuable in scenarios requiring regulatory compliance or when making high-stakes decisions based on data, as it provides clear stopping rules and reduces the risk of false positives.

Learning Resources

• docs: A/B Testing: The Definitive Guide by Optimizely
• course: Statistical Methods for A/B Testing on Coursera
• tutorial: Fixed vs. Sequential Testing Explained by Statsig
• video: Introduction to Hypothesis Testing on Khan Academy