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For iid observations with finite variance and $n>1$, dividing by n-1 makes the sample variance an unbiased estimator for the population variance. This correction addresses the average downward bias that occurs when estimating the mean in the same sample used to calculate the variance.

Conditions: The observations are independent and identically distributed (iid).; The population has finite variance.; The sample size n is greater than 1.

For iid observations with finite variance and $n>1$, dividing by n-1 makes the sample variance an unbiased estimator for the population variance. This correction addresses the average downward bias that occurs when estimating the mean in the same sample used to calculate the variance.

Conditions: The observations are independent and identically distributed (iid).; The population has finite variance.; The sample size n is greater than 1.