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.


I would disagree that one-sided tests are academic and claim that they are more often used in industrial applications. Based on personal experience with journal referees, I would even go so far as to say that there is some bias against one-sided tests in (social science) academia. Most modern textbooks devote very little attention to them. There is some opposition in tech as well. There is a good list of pro and against examples here, going all the way back to Fisher in the 1930s. Equivalentce and non-inferiority tests in medical trials are another example.
While there is no firm boundary between science in academia and industry, the distinction is still useful. I suspect that academic science is more concerned with demonstrating the existence/nonexistence of relationships, which needs two sides. But industrial scientists focus more on directional questions, where one-sided makes more sense and is more efficient. Efficient here means allowing for more/shorter experiments, with quicker feedback on ideas. This efficiency comes at the cost of partially unbounded confidence intervals.
For practical advice, the question should determine the test:

*

*Is A any different from B? $\rightarrow$ two-sided test.


*Is A any worse/better than B? $\rightarrow$ one-sided tests.
Both tests should be combined with pre-registration, ex-ante power calculations, and robustness checks to be safe. Switching to one-sided to get significance after peeking at the data is a bad idea. There is also nothing wrong with running another experiment when you see an effect in the other, unexpected direction.
Questions or claims that produce two-sided tests tend to look like this:

*

*Is there any relationship between Y and X? (existence)

*X has no influence on Y whatsoever (nonexistence)

*X has no relationship with Y (also nonexistence)

*A is not any different from B (nonexistence
again)

One-sided tests come from directional questions:

*

*Is A better than B?

*Is doing X worse than doing Y?

*Is A better than B by at least k?

*Is the change in Y associated with changing X less than m?

Here are two final examples from the business world.
You are evaluating a marketing campaign for your company. You need the added revenue from advertising to exceed the cost of showing the ad. For the decision about launching the campaign, you don't care if the ad drives away customers; you would not launch it anyway. Quantifying the uncertainty about just how terrible the effect is wasteful.
You are considering reducing the number of photos taken per product to lower photography costs and hosting expenses. You need to make sure that the dip in sales is smaller than the savings from shorter photoshoots.
