Practical (not theoretical) examples of where a 1 sided test would be valid? I know that it is generally accepted that the two-sided test is the "Gold Standard". However, I just wanted to see if there are real life, practical applications of the one-sided test in the real world, or if this only exists in academia.
Edit: "Generally accepted / Gold Standard" in the sense of being the default recommendation in the book Introduction to Statistical Learning, 2nd ed., p.558, footnote 8:

A one-sided $p$-value is the probability of seeing such an extreme value of the test statistic; e.g. the probability of seeing a test statistic greater than or equal to $T$=2.33. A two-sided $p$-value is the probability of seeing such an extreme value of the absolute test statistic; e.g. the probability of seeing a test statistic greater than or equal to 2.33 or less than or equal to −2.33. The default recommendation is to report a two-sided $p$-value rather than a one-sided $p$-value, unless there is a clear and compelling reason that only one direction of the test statistic is of scientific interest.


The F-test in a traditional ANOVA is (and should be) one-sided.
(This is not to be confused with using an F-test to compare two group variances, analogous to using a t-test to compare two group means. Such an F-test could be very reasonable.)
Loosely speaking, ANOVA assesses if the variance between the group means overwhelms the variance within the data overall. That is, we care if the "between" variance is greater than the "within" variance. If the "between" variance is less than the "within" variance, then that provides no evidence in favor of our alternative hypothesis that "between" variance is greater than "within" variance.
Consequently, we only look at one side of the F-distribution to calculate the p-value.
