Lessons from a year’s worth of hiring data

So, what are you measuring? I don&#x27;t get it. You made offers mostly to people with few typos in their resume. Is this evidence that people with few typos are good, or evidence that you have some kind of bias against resumes with typos?<p>I find the action words particularly disturbing. Every resume that crosses my desk has people that &#x27;drove&#x27; a &#x27;process&#x27;, and they &#x27;innovated&#x27;, and so on. Buzzword crap. I&#x27;d bet my company on somebody that &quot;implement an image algorithm&quot; (all bad words apparently) over somebody that &quot;enabled client connectivity&quot; any day (all good words).<p>And what is the actual relevance of worked for a big company? Again, I suspect bias on your part. Explain how working for Microsoft is a good predictor for how a person will perform in a 10 person startup?

This is awesome! The biggest threat to validity I see is that your process of filtering from 10 resumes to 1 interview could introduce a lot of bias; if people ignore other warning signs that would weed out the resume because an applicant went to a top school, then you&#x27;re going to end up with more stinkers with that attribute in your interview pool and undervaluing it in your final analysis.<p>I would not suggest that you go through and rate all the unaccepted resumes on the same scale and then regress against both &quot;got an interview&quot; and &quot;got an offer&quot; since that seems like a ton of work, but I think it would be the best way to resolve the threat. :-)

Also, the stuff about MS degrees totally tracks with my subjective experience as a hiring manager - it&#x27;s not a red flag exactly but I don&#x27;t think I&#x27;ve ever been wowed by a person with an MS on their resume.<p>(I&#x27;m sure they do exist, don&#x27;t hate me people with them!)

Correlation vs. causation.