Bootstrapping is used to construct an interval estimate for the population mean when you cannot assume that the population is normally distributed,and the sample size is not large enough to apply the Central Limit Theorem.

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Bootstrapping is a non-parametric resampling method that can be used when the population distribution is unknown or not normal and the sample size is not large enough to rely on the Central Limit Theorem. Bootstrapping involves creating a large number of random samples from the original sample, with replacement, and using these samples to estimate the sampling distribution of the mean. From this sampling distribution, we can construct an interval estimate for the population mean.

Bootstrapping Is Used to Construct an Interval Estimate for the Population