Explorations in statistics: the bootstrap.
Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This fourth installment of Explorations in Statistics explores the bootstrap. The bootstrap gives us an empirical approach to estimate the theoretical variability among possible values of a sample statistic such as the sample mean. The appeal of the bootstrap is that we can use it to make an inference about some experimental result when the statistical theory is uncertain or even unknown. We can also use the bootstrap to assess how well the statistical theory holds: that is, whether an inference we make from a hypothesis test or confidence interval is justified.
Curran-Everett
Douglas
D
Division of Biostatistics and Bioinformatics, National Jewish Health, Denver, Colorado 80206, USA. EverettD@NJHealth.org
D006801:Humans; D015233:Models, Statistical; D012984:Software; D013223:Statistics as Topic / Q000193:education* / Q000379:methods* / Q000639:trends