Originally Posted by

**MagiMaster**
I was wondering about the chi-squared test (specifically, Pearson's chi-squared test). Would it be more informative to perform the test on all *n* datapoints at once, or perform *k* seperate tests on each set of *n/k* datapoints.

An example would be testing a (standard 6 sided) die to see if it's fair. You roll it 600 times, storing each roll. Would you get more information about the fairness of the die from looking at 10 sets of 60 rolls, or 1 set of 600 rolls?

What if, while looking at the results from 10 sets, 4 of those gave a result between .05 and .1 (not low enough to generally be considered a failure on their own, but close)? Would this necessarily imply anything (either about the die or about the results of looking at all rolls)?