Module
Module 4 of 5Lesson 3 of 3~15 min

Bayesian or frequentist, and which tool to choose

Your experimentation tool shows either a probability of winning or a p-value, and the team isn't always sure which one to trust. This lesson explains what each framework really says, how to read their outputs and how to pick a tool based on your volume, data and budget. A free option is covered so you can start without commitment.

Take this lesson: your first course is freeSee the full syllabus

Lesson objective

By the end of this lesson, you will be able to explain to a team the difference between a frequentist and a Bayesian reading of a test, to read a tool's output in both frameworks, and to choose an experimentation tool suited to your constraints, with a free path.

Topics covered

  • Bayesian A/B testing
  • frequentist approach
  • experimentation tools
  • CUPED
  • variance reduction

Where it fits

Analyze the result

How do I read a test result without over-interpreting it or missing a problem?

Lessons in this module

  1. Read a p-value and a confidence interval
  2. Analysis pitfalls: peeking, multiple comparisons, novelty, SRM, Simpson
  3. Bayesian or frequentist, and which tool to choose (this lesson)

What you will learn in the course

This lesson is part of the course Design and analyze an A/B test

  • Decide whether an A/B test is the right method (volume, B2B, reversibility, time, ethics) and choose a suitable alternative otherwise.
  • Write a testable hypothesis, choose a sensitive, attributable primary metric, guardrails and the exposure event to log.
  • Choose the randomization unit and prevent interference between groups, including in B2B.
  • Compute a sample size and a duration from the baseline rate, the minimum detectable effect, the significance level and the power.
  • Analyze a result (p-value, confidence interval, practical significance, guardrails) and read a tool's Bayesian or frequentist output.
  • Detect and avoid analysis pitfalls (peeking, multiple comparisons, novelty effect, sample ratio mismatch, Simpson's paradox).
  • Decide based on rules written before the test and document the experiment in a reusable memo.

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