I was checking on Type I (reject a true H$_{0}$) and Type II (fail to reject a false H$_{0}$) errors during hypothesis testing and got to to know the definitions. But I was looking for where and how do these errors occur in real time scenarios. It would be great if someone came up with an example and explained the process where these errors occur.

2019年11月29日 · You would need to know the population effect size to be able to make statements of Type II errors. In the sketch below, you would need the position of the center of the red curve. In your example, you would need a statement of the anticipated difference in time between cells in anaphase and telophase to be able to evaluate the Type II errors.

Thus, type 1 is this criterion and type 2 is the other probability of interest: the probability that I will fail to reject the null when the null is false. So, 1=first probability I set, 2=the other one.

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2017年1月9日 · "The harm done by tests of significance" relates 3 true stories in which not rejecting the null while it was actually false (so making a Type II error) has been interpreted as accepting the null and had lead to bad general interest …

Type II errors (modify the above procedure as follows): For the second step: Generate a second set of pseudorandom numbers that differ from the first set in a pre-specified way (typically the mean would differ by some amount).

2016年5月10日 · There were bell curves under null and alternative and we could see the trade off between type 1 and type 2 errors. $\endgroup$ – user128949 Commented May 10, 2016 at 2:04

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