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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,
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.
Is the t-test robust to the normality assumption?
The t-test happens to be fairly robust to the normality assumption (as far as the size of the test goes, power and precision can be another matter) so for some cases the combination of “Normal enough” and “Large sample size” means that the sampling distribution is “close enough” to normal that the t-test is a reasonable choice.
Are there any misconceptions about the t test?
The t-test is almost sacred in its imp o rtance and widespread use, so this misconception is a double whammy of both frequency and severity. People think they can’t use the t-test when in reality they can.
Is it better to use Bootstrap for hypothesis testing?
For hypothesis testing it is however better to use a classical bootstrap that computes t -test on every iteration and outputs t -statistics. The reason for that is that t -test does not only compute the difference between means, but also takes into consideration variances of the two groups.
What do you need to know about bootstrapping in statistics?
By Jim Frost 27 Comments. Bootstrapping is a statistical procedure that resamples a single dataset to create many simulated samples. This process allows you to calculate standard errors, construct confidence intervals, and perform hypothesis testing for numerous types of sample statistics.
What is the bootstrap estimate for the standard error in ttest?
The TTEST documentation explains the resampling process and the computation of the bootstrap statistics. The top row of the table shows estimates for the difference of means. The bootstrap estimate for the standard error is 0.45.