So today we’re covering standardized biases and how we would like our standardized biases less than 50%, just as we would like our coverage rates greater than 90%.  You’d be surprised how long it could take to have those two babies work for you.  But you’d also be surprised how magically faster they go after your advisor tells you he’s going on sabbatical to Turkey in three months and wants you to defend before then.  So what is this potent root of many a sleepless nights during my grad school days?  Well, a standardized bias (SB) tells you how big of a difference there is between the true parameter of interest and the average of the parameters obtained from each of your simulations relative to the standard error of those parameters.  So, take our previous example of the probability of Tina being in a justified realm as 12%.  But say the mean probability of her being in such a realm from the 100 generated samples of size 50 as 12.34% and the standard error gotten from each of the 100 probabilities from each sample is 3.96%.  So we take the SB as |12% – 12.34%|/3.96% = 0.0851 or 8.51%. And 8.51% is less than 50% so, umm, yay.  And that also concludes today’s lesson.  But stay tuned as we will cover the root mean square error eventually.  Might take a little break though as I’m attending the Joint Statistical Meetings in Boston. Hey, I hear Boston is becoming the celebrity central of New England so maybe I should take my trilogy with me and if I see anyone from my fantasy cast there, then … yes, I’ll stop.  Till next time, I leave with a beautiful view of the Boston harbor.  But seriously, if I were only to see Matt Damon at Legal