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  • 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 and metric selection
      • 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

Course wrap up: Scientific product development

Summary

You finished Scientific product development and learned to apply the scientific method, from hypothesis to decision, in your product work.

Congratulations! You have finished the Scientific product development course!

Thank you so much for taking this course and helping to build an even stronger experimentation community at your company!

Happy experimenting!

Go back to my learning page to keep learning!

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