A type II error occurs when a null hypothesis that is actually false is incorrectly accepted as true. It represents failing to detect something that is present, like missing a wolf that is actually there. The probability of committing a type II error is represented by beta, and lower beta means higher statistical power to correctly reject a false null hypothesis.

1. 8.12 How does power relate to Type II errors?
Solution
A type II error, also known as an error of the second kind, occurs when the null
hypothesis is false, but it is erroneously accepted as true. It is missing to see what is present, a
miss. A type II error may be compared with a so-called false negative (where an actual 'hit' was
disregarded by the test and seen as a 'miss') in a test checking for a single condition with a
definitive result of true or false. A Type II error is committed when we fail to believe a truth.[1]
In terms of folk tales, an investigator may fail to see the wolf ("failing to raise an alarm"; see
Aesop's story of The Boy Who Cried Wolf). Again, H0: no wolf. The rate of the type II error is
denoted by the Greek letter (beta) and related to the power of a tes