Statistical testing is a task that Data Scientists often need to complete. 5 great guidelines for statistical testing In Python: Lookout for Bad Data, Use your judgement, IGNORE OUTLIERS, Learn more about the test you are performing, For some tests, drop missing values
Some of my favorite rules to follow when working with statistical tests in Python
Statistical testing is a task that Data Scientists often need to complete that can be a lot more error-prone than a lot of the other work a Data Scientist might do. That is to say that in most cases, there is no validation to the validation as there is with a machine-learning model. Most of the time, our probability is a validation of our hypothesis, and there is no backward check that checks to see if our probability is correct, as well. If this is the case, then how do we ever know if our research is statistically significant?
There are some ways to avoid a lot of the common pitfalls in statistical testing that most Data Scientists are likely familiar with that can make the work incredibly reputable and genuine. There are several reasons that a test can be inaccurate, incorrect, or could even be misleading — though statistically true.
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