Hi, All: I have the following problem: My department head wants to accurately estimate class attendance, as a measure of the effectiveness of my teaching. For that, I used to take attendance weekly, i.e., once each week, selecting the day at random, from which I would construct a confidence interval at the end of the year. The department head wants me to switch from counting weekly to doing so bi-weekly, since she must enter the data in the computer , submit, etc. I think this is not a good idea, and I told her so. I wonder what criterion I could use to argue that this swich is likely to cause a non-trvial distortion in the estimate of the true mean weekly attendance. What I have considered, so far: 1) I take the mean of all weekly attendance values, and I construct a confidence interval. Problem (for me) is that the confidence interval will become wider as N:= sample-size decreases. So, fewer measurements means smaller accuracy. 2) Trying a difference-of-means test, between the weekly measurements and the biweekly measurements, and showing that the initial hypothesis is not accepted at, say, a 95% confidence level. 3)Just a general argument that the Law of Large numbers suggests that more measurements I get , the more accurate the estimate will be. Do my arguments work? Should I consider anything else? Thanks for Your Suggestions.