null hypothesis significance testing

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Can someone please help me understand NHST?

I study psychology and would like to have a good basics knowledge of relevant statistics. Thank you.

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Anonymous 0 Comments

Things can happen randomly or for reasons outside of the reasons you are studying.

NHST is where you say “what if the thing I’m studying doesn’t have an effect, so these results I’m seeing are either normal or are from luck?” and you try to check how likely it is that any results you might see aren’t actually from the thing you want to test.

We do it this way because we *don’t know the effect of the thing we are testing* (yet) so it’s hard to prove what our results would look like if it *does* have an effect. How strong do we expect the result to be? We don’t know. But we do know how luck behaves and we do know what “normal” looks like (or we can test to find out) so it’s mathematically much more practical to check if your result looks like normal/lucky or truly extraordinary/significant.

For example you test a new drug and 30% of patients get better. Without the drug, normally 25% of patients get better. So at first glance this looks good. But not *exactly* 25% of patients get better every time you test this. For a small group of people it often ranges from 15%-35% just because of random chance. You can’t say for sure the drug did anything, you would expect a result like this without the drug.

On the other hand maybe you test a larger group. Tested in this way, the 25% “normal” result gets more consistent. With a large group perhaps it is only normal for the result to vary from 22%-28% of patients improving. If you still see 30% of patients recovering after they take the new drug, that suggests it really did something as this is not a result we’d expect to see without the drug due to random chance. Of course, testing with a larger group doesn’t guarantee your drug is going to work, maybe you test it again in these circumstances and now only 25% of the patients taking the drug recover. Your larger test is more accurate and it’s easier to confirm if the result is random or a real effect, but in that last example, you’d say this looks random/normal and it does not show the drug did anything.

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