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 know its generally accepted that the 2 sided test is the Gold Standard

This is highly contextual at best; there are many statistical tests where only one-sided versions of this test are used (e.g., most tests of variance, ANOVA tests, chi-squared tests, etc.).  I presume what you have in mind is the one-sided or two-sided test for something like a test of the mean where both variations exist and are in common use (see this related question and answer).  Assuming this context, the reason that two-sided tests are generally preferred in this context is that one-sided tests sometimes occur when the analyst has used the data to first formulate a one-sided hypothesis, and this biases the test.  Consequently, use of one-sided tests is sometimes viewed with scepticism and raises some immediate questions: Why did you choose to test that one-sided hypothesis instead of the other one?  Was your choice of hypothesis affected by the data?
One of the curious and somewhat unfortunate properties of a classical hypothesis test is that the p-value cannot be compared rationally across different tests.  In particular, when comparing a one-sided and two-sided version of the test, the same evidence in favour of the (one-sided) hypothesis will give a p-value that is half as much in the one-sided test as in the two-sided test.  This means that, if you were to compare p-values across the two tests, the evidence appears to be stronger for the narrower hypothesis, which is of course absurd.  This means that hypothesis tests do not follow the kinds of desiderata we would like them to when you compare across tests, which means we have to be very careful when choosing and interpreting tests.  For this reason, I tend to take a hard line on this issue and require that you should always use the two-sided version of tests of this kind (i.e., tests which have a natural one and two-sided version).  Other statisticians are more liberal on this issue and may be satisfied with a one-sided test if they are confidence that the choice of the side was made a priori and was not affected by the data.
