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The notebook contains 18 tests (the most popular ones, in my opinion) with their goals, assumptions, null and alternative hypotheses and examples. I wrapped those tests in functions, so their usage could be extremely easy: just input the data and get the interpretation of the test as the outcome.
Hypothesis testing can be daunting (yeah, tell me about it).
But the good thing is, it absolutely doesnât have to be (thanks to Python and a bunch of smart people who implement packages for statistical analysis).
If you are here, the odds are you have been googling âHypothesis Testingâ for a while and are already overwhelmed with those stat buzzwords (you know what buzzwords I mean, right? the z-test vs t-tests, paired vs unpaired tests, one-tail vs two-tail tests, one-way vs two-way tests, etc.).
So, I created a simple cheat-sheet that might come handy to you as well. Here is the link.
The notebook contains 18 tests (the most popular ones, in my opinion) with their goals, assumptions, null and alternative hypotheses and examples. I wrapped those tests in functions, so their usage could be extremely easy: just input the data and get the interpretation of the test as the outcome.
Notebook Outline ->
Normality tests
For comparing means
For comparing proportions
For finding relationship
For comparing medians
In some tests, I also calculated the test-statistics without relying on imported packages, so that we can see thereâs no magic happening under the hood.
P.S. I created this notebook for my daily needs, so it doesnât have educational purposes. If you have any comments, suggestions, or if you happen to find any bugs there, let me know :)
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