The following comment by @fsk which I think what you are describing is inaccurate: if you decrease your false negative rate by more than you decrease your fals

The following comment by @fsk which I think what you are describing is inaccurate: if you decrease your false negative rate by more than you decrease your false positive rate, you're actually hiring MORE bad candidates First false negatives (FN) and false positives (FP) are independent of each other. FP estimates how many bad developers you would end up having regardless of your FN. The FN determines how many good developers you would turn away regardless of your FP. If you are confused about this, well, these numbers are part of appropriately called "Confusion Matrix". I would highly recommand reading up on Wikipedia (http://en.wikipedia.org/wiki/Confusion_matrix http://en.wikipedia.org/wiki/Confusion_matrix) or any textbooks before you jump on commenting and through bayesian equation around because you are certainly not using right terminology. Also both of these are again independent of actual % of bad developers out there (i.e. whether market has 99% bad or 1% doesn't matter, FP solely determines what many bad developers you would end up with). Next, it might be actually easier for you to think in terms of precision and recall instead of FP/FN. Interviewing process is nothing but classification problem and P/R is standard way to measure its performance. Again Wikipedia is your friend to brush up on that. A classic situation in classifier performance is referred to as precision recall tradeoff. You can plot that on curve called RoC and choose your operating point. The way you typically do that is by quantifying how much you would get hurt due to loss in precision (~ more FP) compared to increase in recall (~ less FN). You plug the costs in equation and decide your operating point. For companies that can rapidly deal with FP, increasing recall may make sense and other way around. However in most cases there are too many other reasons that I'd listed should typically prevent you from lowering your precision too much.