What is a bootstrapped t test?
Bootstrapping is any test or metric that uses random sampling with replacement (e.g. mimicking the sampling process), and falls under the broader class of resampling methods. Bootstrapping assigns measures of accuracy (bias, variance, confidence intervals, prediction error, etc.) to sample estimates.
Does bootstrap require independence?
The bootstrap is often used for nonparametric inference. However, in some cases, it is useful to bootstrap and then conduct parametric tests within each resample (optionally, see References, but this is not required reading in any way for the question).
How are the bootstrap and the t-test similar?
Both the t-test and the bootstrap are based on sampling distributions, what the distribution of the test statistic is. The exact t-test is based on theory and the condition that the population/process generating the data is normal. The t-test happens to be fairly robust to the normality assumption (as far as the size of the test goes,
Can a bootstrap give you the sampling distribution?
The bootstrap does not give you normality, it gives you the sampling distribution (which sometimes looks normal, but still works when it is not) without needing the assumptions about the population. For t-tests where it is reasonable to assume that the population is normal (or at least normal enough) then the t-test will be best (of the 2).
What are the disadvantages of the bootstrap method?
The disadvantage of the bootstrap is that it is very dependent on the sample representing population because it does not have the advantages of other assumptions.
How are bootstrapping and traditional methods used in statistics?
Both bootstrapping and traditional methods use samples to draw inferences about populations. To accomplish this goal, these procedures treat the single sample that a study obtains as only one of many random samples that the study could have collected.