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Questions and Answers
Which type of testing involves comparing two or more versions of a page or feature?
Which type of testing involves comparing two or more versions of a page or feature?
- Fake Door Testing
- Side-by-Side Testing
- A/A/B Testing
- A/B Testing (correct)
When is A/A/B testing typically used?
When is A/A/B testing typically used?
- When validating a new variant against two controls
- When needing to explore a problem or generate hypotheses (correct)
- When needing to quantify a problem or solution
- When comparing two or more versions of a page/feature
When is A/A/B testing typically used?
When is A/A/B testing typically used?
- When Identifying Trends and Patterns in User Engagement
- When a More Rigorous Validation is Required (correct)
- When Comparing User Preference Between Two or More Versions
- When Testing User Interest in a Feature Before Development
When is testing user interest in a feature before development conducted?
When is testing user interest in a feature before development conducted?
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Study Notes
Research Methods
- Criteria: Quantitative, Qualitative, A/B Testing, A/A/B Testing, Fake Door Testing, Side-by-Side Testing, Cohort Analysis, Mixed Methods Approach
Quantitative vs. Qualitative Research
- Quantitative Research: Focuses on numerical data, statistical analysis, and generalizability
- Qualitative Research: Focuses on textual and visual data, thematic analysis, and understanding/exploration
Types of Testing
- A/B Testing: Compares two versions of a page/feature to determine which performs better
- A/A/B Testing: Tests two identical versions of a page/feature to ensure testing methodology is sound
- Fake Door Testing: Tests user interest in a feature before investing resources
- Side-by-Side Testing: Tests two versions of a page/feature simultaneously
- Cohort Analysis: Analyzes user behavior over time to identify trends and patterns
Data Types and Goals
- Numerical Data: Focuses on statistical analysis, prediction, and optimization
- Textual/Visual Data: Focuses on understanding, exploration, and user behavior
- Categorical Data: Focuses on user behavior and preferences
- Goal: Generalizability, prediction, understanding, exploration, optimization, and comparison
Tools and Techniques
- Surveys, Experiments: Used for quantitative research
- Interviews, Observations: Used for qualitative research
- Split URL, Multivariate: Used for A/B testing
- Extended A/B Testing: Used for comprehensive understanding
- Landing Pages, Analytics: Used for behavioral analytics
- User Interface Tools: Used for user behavior analysis
Sample Size and Analysis
- Sample Size: Large (quantitative), small to moderate (qualitative), moderate to large (A/B testing)
- Analysis: Statistical analysis (quantitative), thematic analysis (qualitative), statistical comparison (A/B testing)
Time Frame and When to Use
- Time Frame: May require more time (quantitative), often quicker (qualitative), short to medium-term (A/B testing)
- When to Use: Quantitative research for generalizability and prediction, qualitative research for exploration and understanding, A/B testing for optimization and comparison, cohort analysis for behavioral trends analysis.
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