When utilizing hypothesis testing, what are type I and type II errors, and which error would you say is more detrimental?

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Anonymous · Accepted Answer

Type 2 error is when you fail to reject the null hypothesis - that is saying
that there is no difference, when there exists a difference. Type 1 error is
when you falsely reject a null hypothesis. That is, you perceive a difference
when there isn't actually any. I believe type 1 errors are worse, because you
wrongly assume that your change has a positive impact and you go ahead and
implement it which is costly but actually has no impact.

When utilizing hypothesis testing, what are type I and type II errors, and which error would you say is more detrimental?

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