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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
      • 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
    • 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: 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
    • Working with 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
    • Videos

Introduction to feature flags

In this course, you will learn about feature flags and how to use them effectively in your applications.

Note

There are reader exercises at the end of each lesson that help you check your understanding. Answer all questions correctly to finish the lesson and track your progress.

This course consists of the following lessons:

Lesson 1: What is a feature flag

In this lesson, you learn about what a feature flag is and what you can use them for.

Not completed

Lesson 2: Life cycle

In this lesson, you learn about the life cycle of a feature flag. This includes when to create, update, and delete feature flags.

Not completed

Lesson 3: Clients

In this lesson, you learn about what a client is and why it is often helpful to use different clients for different parts of your application.

Not completed

Lesson 4: Evaluation context and targeting

In this lesson, you learn about the evaluation context and targeting of feature flags, so that you can control which users see which flag variant based on their context.

Not completed

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