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The use of n-1 instead of n degrees of freedom fixes this because the lower the degrees of freedom of a chi-square distribution the tighter the distribution. This slightly tighter distribution makes up for our under-estimate of the the true population variance.
For example, the degrees of freedom formula for a 1-sample t test equals N ? 1 because you're estimating one parameter, the mean. To calculate degrees of freedom for a 2-sample t-test, use N ? 2 because there are now two parameters to estimate.
The reason dividing by n-1 corrects the bias is because we are using the sample mean, instead of the population mean, to calculate the variance. Since the sample mean is based on the data, it will get drawn toward the center of mass for the data.
Degrees of freedom are always the number of units within a given set minus 1. It is always minus one because, if parameters are placed on the data set, the last data item must be specific so all other points conform to that outcome.
In statistics, Bessel's correction is the use of n ? 1 instead of n in the formula for the sample variance and sample standard deviation, where n is the number of observations in a sample. This method corrects the bias in the estimation of the population variance.