Skip to main content


Edited by: Published: 2017
+- LessMore information
Download PDF

Bootstrapping is an approach to properties of statistics, such as sampling variances, standard errors, and confidence intervals, that does not rely on a particular assumption about the shape of the distribution around a given statistic. Bootstrapping is therefore said to be a nonparametric approach to statistical inference. It can be particularly useful when the researcher does not know the theoretical distribution of a given test statistic or when no such distribution exists.

Bootstrap methods for evaluating statistics rely on data-based simulations wherein the observed data stand in for the population of interest. Measures of uncertainty around a statistic that are obtained via the bootstrap therefore might be thought of as being drawn from samples of a given sample, as bootstrapping is a computationally ...

Looks like you do not have access to this content.

Reader's Guide

  • All
  • A
  • B
  • C
  • D
  • E
  • F
  • G
  • H
  • I
  • J
  • K
  • L
  • M
  • N
  • O
  • P
  • Q
  • R
  • S
  • T
  • U
  • V
  • W
  • X
  • Y
  • Z

      Copy and paste the following HTML into your website