Why is bootstrapping a good way to ensure robust confidence intervals and significance values?
A)Because it does not rely on assumptions of Type I errors.
B)Because it relies on assumptions of normality and error.
C)Because it does not rely on assumptions of normality or homoscedasticity.
D)Because it relies on assumptions of heteroscedasticity and normality.

Question

Quizplus · Accepted Answer

Bootstrapping is a non-parametric method that does not rely on assumptions of normality or homoscedasticity. Instead, it generates thousands of resamples from the original data to create a distribution that can be used to calculate confidence intervals and significance values. This can provide more robust results, especially when the underlying assumptions of traditional statistical methods are not met.

Why Is Bootstrapping a Good Way to Ensure Robust Confidence