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This is assuming your experiment was designed to test your hypothesis and that you model your null hypothesis properly. A p value means nothing anyway until you
by ta988 4y ago
This is assuming your experiment was designed to test your hypothesis and that you model your null hypothesis properly. A p value means nothing anyway until you have both of these. Add to that the fact that many wrongly consider that a high p value disproves the original hypothesis.
- OrangeMonkey 4y agoI think you got it - there are many ways for studies to fail and its hard, at this time, to validate that a study succeeded except multiple studies confirming the original finding. Even then, I would worry that the results may be caused by some confounder in the original dataset/design instead of something you can trust.