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The logic for the identification of the best distribution fit in the get_best() function when the ks_statistic method is selected is backwards given the null hypothesis of the KS test. It looks like the get_best() function is returning the distribution with the minimum KS p-value. But the null hypothesis of the KS test is that the sample is distributed according to the reference distribution. And selecting the model with the minimum KS p-value means we're potentially picking the worst fitting distribution.
The text was updated successfully, but these errors were encountered:
The logic for the identification of the best distribution fit in the get_best() function when the ks_statistic method is selected is backwards given the null hypothesis of the KS test. It looks like the get_best() function is returning the distribution with the minimum KS p-value. But the null hypothesis of the KS test is that the sample is distributed according to the reference distribution. And selecting the model with the minimum KS p-value means we're potentially picking the worst fitting distribution.
The text was updated successfully, but these errors were encountered: