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Show HN: Tea-tasting, a Python package for the statistical analysis of A/B tests
Advantages of using tea-tasting for the statistical analysis of experiments.
Student's t-test, Bootstrap, variance reduction with CUPED, power analysis, and other statistical methods and approaches out of the box. In addition, tea-tasting provides some convenience methods and functions, such as pretty formatting of the result and a context manager for metric parameters. In this experiment, Fisher developed the null hypothesis significance testing framework to analyze a lady's claim that she could discern whether the tea or the milk was added first to the cup.
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