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.


One sided significance tests are useful most of the time that you want an index of the strength of evidence in the data against the null hypothesis according to the statistical model. That is to say, most of the time that a significance test might be used. That means that practical examples abound, even where they mostly illustrate how two-sided testing is used where one-sided is at least as appropriate. People frequently use P-values from two-sided tests because they are expected to do so, not because one sided tests are inappropriate.
Standard arguments in favour of two-sided testing are almost exclusively relevant to hypothesis testing, not significance testing. If you are unsure why I would draw a distinction between hypothesis testing and significance testing then you should start reading about that before you try to sort out the number of tails you should be testing against. See this question for a good start: What is the difference between "testing of hypothesis" and "test of significance"?
When you are thinking about the evidential meaning of data according to a statistical model it is natural to look at likelihood functions, and indeed there is a one to one relationship between significance test-derived P-values and likelihood functions. Two-sided P-values point to likelihood function that are bimodal whereas the natural interpretation of evidence would yield unimodal likelihood functions from those same data. The one-sided P-value is the index to those unimodal likelihood functions. See here for a full explanation of the relationship between P-values and likelihood functions: https://arxiv.org/pdf/1311.0081.pdf
This chapter explains the distinction between significance tests and hypothesis tests and how P-values can be used to support scientific inferences: https://rest.neptune-prod.its.unimelb.edu.au/server/api/core/bitstreams/1d20d0cb-f3d3-5e23-be16-c20b735f8568/content
