Statistical-Hypothesis-Testing-with-Python

Quick-reference guide to the 17 statistical hypothesis tests that you need in applied machine learning, with sample code in Python.

There are 100s of hypothesis testing types existing in field of statistics but we will need the 17 statistical hypothesis testing that we would use in applied machine learning and the machine learning projects.

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A[STATISTICAL HYPOTHESIS] --> B(NORMALITY TESTS);
A[STATISTICAL HYPOTHESIS] --> C(CORRELATION TESTS);
A[STATISTICAL HYPOTHESIS] --> D(STATIONARY TESTS);
A[STATISTICAL HYPOTHESIS] --> E(PARAMETRIC STATISTICAL TESTS);
A[STATISTICAL HYPOTHESIS] --> F(NON-PARAMETRIC STATISTICAL TESTS);

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Details about the above named test and their sub branches

1. NORMALITY TESTS

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   A[NORMALITY TESTS] --> B(Shapiro-Wilk Tests)
   A[NORMALITY TESTS] --> C(D'Agostino's K^2 Test)
   A[NORMALITY TESTS] --> D(Anderson-Darling Test)
   

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2. CORRELATION TESTS

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   A[CORRELATION TESTS] --> B(Pearson's Correlation Coefficient)
   A[CORRELATION TESTS] --> C(Spearman's Rank Correlation)
   A[CORRELATION TESTS] --> D(Kendall's Rank Correlation)
   A[CORRELATION TESTS] --> E(Chi-Squared Test)
   

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3. STATIONARY TESTS

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   A[STATIONARY TESTS] --> B(Augmented Dickey-Fuller)
   A[STATIONARY TESTS] --> C(Kwiatkouski-Phillips-Sehmidt-Shin)
   

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4. PARAMETRIC STATISTICAL HYPOTHESIS TESTS

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   A[PARAMETRIC STATISTICAL HYPOTHESIS TESTS] --> B(Student's t-test)
   A[PARAMETRIC STATISTICAL HYPOTHESIS TESTS] --> C(Paired Student's t-test)
   A[PARAMETRIC STATISTICAL HYPOTHESIS TESTS] --> D(Analysis of Variance test/ANOVA)
   A[PARAMETRIC STATISTICAL HYPOTHESIS TESTS] --> E(Repeated Measures ANOVA test)
   

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5. NON-PARAMETRIC STATISTICAL HYPOTHESIS TESTS

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   A[NON-PARAMETRIC STATISTICAL HYPOTHESIS TESTS] --> B(Mann-Whitney U test)
   A[NON-PARAMETRIC STATISTICAL HYPOTHESIS TESTS] --> C(Wilcoxon Signed-Rank test)
   A[NON-PARAMETRIC STATISTICAL HYPOTHESIS TESTS] --> D(Kruskal-Wallis H Test)
   A[NON-PARAMETRIC STATISTICAL HYPOTHESIS TESTS] --> E(Friedman Test)
   

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