Great insight. Thank you. Now, just for the avoidance of confusion, the standard deviation in this context is the standard deviation of the measurement error,

Great insight. Thank you. Now, just for the avoidance of confusion, the standard deviation in this context is the standard deviation of the measurement error, not the standard deviation of the skill level of the applicants. For example, if you simplistically assume people can be graded on a 1d scale from 0 to 10, and you think a certain job needs someone with a skill level of 7.5, and your interviewing process has an uncertainty of 1, then you need to set the bar at 9.5. From the outside, people would wonder why you are trying to hire Einsteins for a job to write dashboards. You are not trying to hire Einsteins, it's just that your interviewing process sucks. If you manage to reduce that estimation uncertainty from 1 to 0.25, then you need to set the bar at 8. All of a sudden your pool of reasonable applicants increases tremendously. My conclusion (I'm making it up as I go, so feel more than free to discard it - I may discard it tomorrow, who knows?) is that there's tremendous value for companies to improve their interviewing process. But there's a second conclusion: as an interviewee, you want to assist the interviewer in reducing their estimate uncertainty. Because that uncertainty is not a fixed number, it's candidate-dependent. A company will prefer to hire a candidate that is an 7 plus or minus 0.5 than an 8 plus or minus 2. All of a sudden you will beat much stronger candidates. How can you do that? I'm not sure, if I knew I'd probably be 10 times richer. But I can offer some guesses. If you have publications, a GitHub account with nice projects, maybe some youtube videos where you explain some concepts quite well. Also, anytime someone can vouch for you, someone that the potential employer trusts, that reduces their uncertainty a lot.