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How to avoid P hacking
Some data practices can lead to statistically dubious findings. Here’s how to avoid them.
It might get you a publication in the short term, but P hacking contributes to the reproducibility and replicability crisis in science by filling the literature with dubious or unfounded conclusions. In these cases, it is best practice to go back to the original data or laboratory notes to determine whether experimental conditions could explain this outlier. Perhaps you pipetted double the amount of reagent into your sample, or construction work nearby during the time you were testing that animal affected its behaviour.
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