Confidence
  • Documentation
  • Blog
  • Bootcamp
  • Status
  • Confidence Bootcamp
    • My learning
    • Intro to experimentation
      • Introduction
      • Lesson 1: Why you should experiment
      • Lesson 2: Experiment hypothesis
      • Lesson 3: Success and guardrail metrics
      • Lesson 4: Success metrics
      • Lesson 5: Set up your experiment
      • Lesson 6: Calculation frequency
      • Lesson 7: Target audience
      • Lesson 8: Sample size
      • Lesson 9: Quality assurance
      • Lesson 10: Run your experiment
      • Lesson 11: Evaluate your experiment and make a decision
      • Lesson 12: A/B tests and rollouts
      • Course wrap up
    • Intro to metrics
      • Introduction
      • Lesson 1: What is a metric?
      • Lesson 2: Metric roles
      • Lesson 3: Time considerations
      • Lesson 4: Capturing behavior
      • Lesson 5: Strategic metrics
      • Lesson 6: Interpretability
      • Lesson 7: Feasibility and sensitivity
      • Lesson 8: Variance reduction
      • Lesson 9: Select metrics
      • Lesson 10: Segment-level analysis
      • Course wrap up
    • Scientific product development
      • Introduction
      • Lesson 1: Why you should experiment
      • Lesson 2: The scientific method
      • Lesson 3: Randomized controlled trials
      • Lesson 4: Experiment hypothesis
      • Lesson 5: Case study
        • Case study
        • Answers to case study
      • Lesson 6: Why do we need statistics?
      • Lesson 7: Success metrics
      • Lesson 8: Detectable effects and sample size
      • Lesson 9: Make a decision
      • Course wrap up
    • A primer on hypothesis testing
      • Introduction
      • Lesson 1: Introduction to hypothesis testing
      • Lesson 2: True vs estimated effects
      • Lesson 3: Sampling distribution of the difference-in-means estimator
      • Lesson 4: Z-tests and how to reject the null hypothesis
      • Lesson 5: False postive rate and alpha
      • Lesson 6: True positive rate, MDE, and power
      • Course wrap up
    • Intro to Feature Flags
      • Introduction
      • Lesson 1: What is a feature flag?
      • Lesson 2: Lifecycle of a feature flag
      • Lesson 3: Clients
      • Lesson 4: Evaluation context and targeting
    • Sample size calculation - I
      • Introduction
      • Lesson 1: What is the required sample size?
      • Lesson 2: Alpha and power
      • Lesson 3: Baseline mean and variance
      • Lesson 4: Sample size playground - I
    • Sample size calculation - II
      • Introduction
      • Lesson 1: Multi-metric decision making
      • Lesson 2: Number of success metrics
      • Lesson 3: Number of guardrail metrics
      • Lesson 4: Number of comparisons
      • Lesson 5: Sample size playground - II
    • Sample size calculation - III
      • Introduction
      • Lesson 1: Binary metrics
      • Lesson 2: Treatment group proportions
      • Lesson 3: Variance reduction
      • Lesson 4: Sequential testing and sample size
      • Lesson 5: Sample size playground - III
    • Advance your experimentation
      • Introduction
      • Lesson 1: Guardrail metrics with non-inferiority margins
      • Lesson 2: Choose evaluation frequency
      • Lesson 3: Metrics' roles in experiments
      • Lesson 4: Cumulative holdback evaluations
    • Experimentation culture
      • Introduction
      • Lesson 1: Onboarding into experimentation
      • Lesson 2: Empowering experimentation champions
      • Lesson 3: Sustaining the experimentation culture
    • Videos

Lesson 4: Sample size calculation playground - level I

Summary

In this lesson, you build intuition for sample size calculation by using an interactive sample size calculator playground. By changing parameters that we have discussed in this course, you can see how the sample size changes.

This is the first level of the sample size courses, so a lot of the parameters that you can affect in a real experiment are fixed here and cannot be changed.

The idea with this lesson is simple, you change the parameters in the sample size calculator and see how the sample size changes. In this lessons there are more questions than for usual lessons. Most can be answered by simply trying things out in the sample size calculator.

Recommendation

Turn on the 'Show detailed formulas' option in the sample size calculator to see the formulas used to calculate the sample size. This can help you understand how the different parameters affect the sample size.

Required sample size calculator (level 1)

Metric parameters

Statistical parameters

Required Sample Size: 0

Reader exercise

If you double the relative MDE/NIM (from 1% to 2%), what happens to the required sample size?

Reader exercise

What happens to the required sample size if you increase the baseline variance while keeping all other parameters constant?

Reader exercise

If you want to increase the power from 0.8 to 0.9, what happens to the required sample size?

Reader exercise

If power is 80% and alpha is 10%, what increases the sample size the most?

Reader exercise

If your baseline variance is very small (near zero), what tends to happen to the required sample size?

Notes for Nerds

In Confidence

The Confidence sample size calculator takes into account a whole range of parameters when calculating the required sample size. If you want to learn about how Confidence handles risk management, you can read more about it in this blog post.

Was this page helpful?

PreviousLesson 3: Baseline mean and variance
NextIntroduction

© Copyright 2026. All rights reserved.

Follow us on TwitterFollow us on GitHub

On this page

  1. Notes for Nerds