What is more important for the researcher to be concerned about in a study, Type I or Type II errors? Why?

Question

This is a statistics question. Okay, all you geniuses, what does this mean? :o) Thanks for your help!

Answer 1

In a hypothesis test, a type I error occurs when the null hypothesis is rejected when it is in fact true. It's a false positive.

Type II error occurs when the null hypothesis is not rejected when it should have been.

A type I error is often considered to be more serious, and therefore more important to avoid, than a type II error.  For example, our court system is designed around the hypothesis that all defendants are considered innocent until they are proven guilty.  We want to minimize the number of false positives (innocent people who are convicted) even if it means we may have some false negatives (guilty people who are not convicted).

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Answer 2

Type I - importance to researcher:

Researcher studies ability to produce new medication on production line assuring purity and collects data supporting Type I error < 0.0001   (0.01%)    [very high probability of detecting impure "bad" product]

Type II - important to researcher:

Researcher studies ability to announce to patients of "escaped" impure product from production line and learns that 0.98 proportion will not learn of announcement. [low probability of detecting pure "good"

product which is truely "bad"]

Quite commonly, Type I errors are highly likely detected (e.g. impure product). And also very commonly, "escapes" are unlikely to be detected.