A/B Testing Practice Exam
A/B Testing Practice Exam
About A/B Testing Exam
The A/B Testing Exam evaluates your ability to design, execute, and interpret controlled experiments used to compare variations of web pages, products, or features. It covers statistical foundations, test structuring, result analysis, and optimization strategies. Ideal for digital marketers, data analysts, product managers, and UX professionals aiming to make data-driven decisions.
Who should take the Exam?
This exam is ideal for:
- Digital marketers and CRO specialists working on performance optimization
- Product managers seeking to validate feature impact
- UX/UI designers aiming to enhance user experience through testing
- Data analysts and business intelligence professionals using experiments for insights
- Startup founders and growth hackers validating product hypotheses
Skills Required
- Basic understanding of statistics and data interpretation
- Familiarity with web analytics tools (e.g., Google Analytics, Mixpanel)
- Conceptual knowledge of conversion funnels and user behavior
- Experience with A/B or multivariate testing platforms
Knowledge Gained
- Ability to plan and structure A/B tests with control and variants
- Understanding statistical significance, p-values, and confidence intervals
- Proficiency in interpreting test results and making data-backed decisions
- Familiarity with common A/B testing tools and testing pitfalls
Course Outline
The A/B Testing Exam covers the following topics -
Domain 1 - Introduction to A/B Testing
- What is A/B testing and why it matters
- Key terms: control, variant, conversion rate
- Real-world use cases across industries
Domain 2 - Designing Effective A/B Tests
- Defining a hypothesis and measurable goals
- Selecting the right metrics
- Test types: A/B, A/B/n, multivariate, split URL testing
Domain 3 - Statistical Concepts in A/B Testing
- Significance level, confidence interval, power
- Understanding sample size and test duration
- Interpreting p-values and avoiding false positives
Domain 4 - Tools and Platforms
- Overview of popular tools: Google Optimize, Optimizely, VWO
- Setting up tests, tracking goals, and analyzing results
- Integration with analytics and CRM tools
Domain 5 - Analyzing and Interpreting Results
- Reading reports and drawing insights
- When to stop or extend tests
- Post-test analysis and decision-making
Domain 6 - Advanced Strategies and Pitfalls
- A/A testing for baseline noise
- Common mistakes in testing (peeking, segment bias, etc.)
- Multivariate testing vs. A/B testing
- Running tests at scale and test prioritization
