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Bonferroni effects. In the parent article referred to as researcher degrees of freedom, in pop culture referred to as p-hacking. In machine learning, it's referred to as overfitting. They are all the same phenomenon, of having more than one hypothesis, and then using statistical measures that assume that you only have one hypothesis to claim confidence. Journals only publishing positive results is an example of this phenomenon. Changing your hypothesis to match your data is another such example.