What correction is applied when performing variance calculations on a sample by subtracting 1 from the total number of values?

The correction applied when calculating the variance of a sample by subtracting 1 from the total number of values is known as Bessel's correction. This adjustment is made to provide an unbiased estimate of the population variance when using a sample.

When you calculate the sample variance, you divide the sum of squared differences between each data point and the sample mean by n-1 (where n is the number of observations in the sample) rather than by n. The reason for this adjustment is that using n tends to underestimate the population variance, particularly in small samples. By applying Bessel's correction, the variance calculation incorporates an adjustment that compensates for the bias that occurs when a sample is used instead of the entire population. This leads to a more accurate estimation of the population variance.

The other corrections mentioned do not pertain directly to this specific adjustment in variance calculations for samples. For example, Student's t correction relates to estimating confidence intervals when sample sizes are small. Fisher's correction refers to adjustments made in statistical analysis, particularly in ANOVA, and normality correction might refer to methods aimed at adjusting data to fit a normal distribution rather than addressing variance specifically.