What statistical technique is used to estimate variance when data does not follow a normal distribution?

Bootstrapping is the appropriate statistical technique to estimate variance when data does not follow a normal distribution. This method is particularly useful in situations where traditional parametric assumptions (like normality) are violated. Bootstrapping involves repeatedly resampling from the observed data sample, with replacement, to create a large number of simulated samples. By calculating the statistic of interest (such as the mean or variance) for each of these resampled datasets, researchers can generate an empirical distribution of the statistic and derive confidence intervals and bias estimates that do not rely on normality.

Other statistical methods like regression analysis, ANOVA, and correlation analysis typically assume that data follows a normal distribution in their inferential procedures. Consequently, applying these techniques to non-normally distributed data may lead to misleading results. In contrast, the bootstrapping technique is flexible and robust, making it a valuable tool for researchers dealing with non-normal data distributions in their analyses.