Used Bayes in the wild.

It was really a textbook case. I had a short story under review at Asimov's SF magazine, and they'd held onto it for over two months. Per Duotrope (a writer's market site), Asimov's takes twice as long on average reviewing stories that are ultimately accepted as stories that are ultimately rejected (the likely explanation is that obviously bad stories can be rejected right away, while stories that eventually get bought are handed around to multiple readers). So I sort of casually assumed without really thinking about it that this meant I was much more likely to get my story accepted.

But wait! Duotrope has various response statistics based on reports from users. With a few assumptions, I could use their numbers for a simple Bayes calculation, and figure out the real chances for an acceptance given a two month wait.

I used as my priors the numbers on Duotrope for acceptance rate (P(Acceptance)), mean reply time and std dev of reply time (used for P(Two Month Wait)), and took a guess at P(TMW|A) based on the mean reply time for acceptances. The result: yes, it was more likely that my piece was accepted, but because P(A) was so low to begin with (less than .5%), P(A|TMW) was still really low (less than 1%).

I calibrated my expectations accordingly. Which was just as well, since I got their rejection the next day. Rejections are always disappointing, but I'd have been far more disappointed if my expectations had still been out of joint.