Bootstrap resampling helps estimate confidence intervals without assuming what?

Bootstrap resampling helps estimate confidence intervals without assuming what?

• A. A specific theoretical sampling distribution.
• B. Independence of observations.
• C. Normality of the data.
• D. Homogeneity of variance.

Answer: (A)

Bootstrap resampling estimates confidence intervals by repeatedly drawing samples with replacement from the observed data and recalculating the statistic. This builds an empirical sampling distribution directly from the data, so you don’t have to assume any specific theoretical form for how the statistic should behave. In other words, you don’t rely on a normal or other parametric distribution to form intervals. The main requirement is that the sample should be reasonably representative of the population (roughly i.i.d. for the simplest bootstrap).

Bootstrap resampling estimates confidence intervals by repeatedly drawing samples with replacement from the observed data and recalculating the statistic. This builds an empirical sampling distribution directly from the data, so you don’t have to assume any specific theoretical form for how the statistic should behave. In other words, you don’t rely on a normal or other parametric distribution to form intervals. The main requirement is that the sample should be reasonably representative of the population (roughly i.i.d. for the simplest bootstrap).