Hypothesis Testing: Alpha, Beta, Power, MDE, Standard Error, Critical Value, Sample Size. Explained!

Hypothesis testing is taught wrong in our textbooks because they often inconsistently blend Fisher's significance test and Neyman-Pearson's hypothesis testing. This tutorial breaks down this convoluted topic and explains the concepts incrementally, using visualizations and first principles. It's a must-watch for all data scientists. 00:00 The Importance of Hypothesis Testing 02:46 The Null Hypothesis, alpha, and the critical value 06:41 The Alternative Hypothesis, beta, and power 09:26 Statistical power explained in three ways 11:23 Minimum Detectable Effect (MDE) and sample size 14:26 Key Takeaways and Practical Applications 15:41 Conclusion and Future Content Blog: https://www.statsig.com/blog/hypothesis-testing-explained "Misinterpretation and abuse of statistical tests, confidence intervals, and statistical power have been decried for decades, yet remain rampant. A key problem is that there are no interpretations of these concepts that are at once simple, intuitive, correct, and foolproof." -- Greenland et al (2016)