18 ms·
The "intuitive" explanation I've heard is that (N-1) is the number of degrees of freedom of a sample with N data points, so when you divide by (N-1) you're norm
by lmkg 16y ago
The "intuitive" explanation I've heard is that (N-1) is the number of degrees of freedom of a sample with N data points, so when you divide by (N-1) you're normalizing some valuation of the "spread" of the sample against a measure of the potential for the sample to "spread."
I think how "intuitive" this idea is depends on how well you understand thermodynamics. I, clearly, do not.
- tel 16y agoI've heard that description before and find it more ad hoc than intuitive. Is there some valid mathematical object K such that often times you will normalize unbiased estimators by N/(N-K)? Is this K sometimes equal to some measure of the number of equations/constraints you have? Does this seem to hold a lot of force in ANOVA especially? Sure, sure, sure. I still don't see why this normalization is required to make a better estimate (excepting when you define "better" as an unbiased estimator, maybe the MVUE).