| 1 -Analyzing Results and Decision Making.mp4 | 87.3 MB | ||
| 1 -Basic Concepts of Hypothesis Testing.mp4 | 67.1 MB | ||
| 1 -Congratulations and Next Steps.mp4 | 8.9 MB | ||
| 1 -Defining Hypotheses and Success Metrics.mp4 | 88.7 MB | ||
| 1 -Hello.mp4 | 13.2 MB | ||
| 2 -Experiment Design and Segmentation.mp4 | 86.7 MB | ||
| 2 -Probability Distributions and the Normal Distribution.mp4 | 76.9 MB | ||
| 2 -Sequential Testing and Multi-Armed Bandits.mp4 | 104.5 MB | ||
| 3 -Personalization and Targeting.mp4 | 81 MB | ||
| 3 -Randomization and Avoiding Bias.mp4 | 75.7 MB | ||
| 3 -Sampling and Sampling Distributions.mp4 | 105.1 MB | ||
| 4 -Confidence Intervals and Margin of Error.mp4 | 69.5 MB | ||
| 4 -Integration with Product Roadmaps.mp4 | 212.4 MB | ||
| 4 -Test Variants and Traffic Allocation.mp4 | 112.7 MB | ||
| 5 -Organizational Buy-in and Culture.mp4 | 89.5 MB | ||
| 5 -Running Tests and Data Collection.mp4 | 74.3 MB | ||
| 5 -Statistical Power and Sample Size Calculation.mp4 | 96 MB | ||
| 6 -Ethical and Privacy Considerations.mp4 | 103.1 MB | ||
| 6 -Monitoring and Stopping Rules.mp4 | 96.2 MB | ||
| 6 -Type I and Type II Errors.mp4 | 109.1 MB | ||
| 7 -Bayesian vs Frequentist Approaches.mp4 | 62.3 MB | ||
| 7 -Common Pitfalls in A B Testing.mp4 | 104 MB | ||
| 7 -Scaling A B Testing Programs.mp4 | 86.3 MB | ||
| 8 -Interpreting P-values and Common Misconceptions.mp4 | 82.9 MB | ||
| 8 -Tools and Platforms for A B Testing.mp4 | 112.3 MB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 27 total files | |||
A/B Testing Mastery: Statistical Foundations & Strategy
https://WebToolTip.com
Published 5/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 4h 46m | Size: 2.15 GB
Learn hypothesis testing, experiment design, and strategic A/B implementation to optimize user experiences
What you'll learn
Define and formulate testable hypotheses and success metrics for A/B experiments
Calculate sample sizes, statistical power, and interpret confidence intervals to design reliable tests
Differentiate between Type I & II errors and apply Bayesian and frequentist methods for valid analysis
Implement randomization, segmentation, and traffic allocation to eliminate bias and optimize results
Requirements
Basic understanding of statistics, familiarity with spreadsheets or statistical software (Excel, Python, R), and access to an A/B testing platform or simulation environment
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