Thus, you can accept the large standard errors or collect more data and increase your n. Your sample size is small (i.e., n =50) so presumably you have larger standard errors than you would like. It is a tool to, among other things, give an estimate of standard error. If this mean is very different from 8, then this suggests that the sample median is biased for estimating the median of our bootstrap population. This is very wrong! It also may help to explain some people's naive mistrust in the concept of bootstrapping.īootstrapping is not a tool to make standard errors smaller. Calculate the mean of the 10000 bootstrap sample medians. I remember reading a journal article where an author artificially inflated their sample size using bootstrapping and reported the resulting p-values. All non-zero coefficients will by definition be statistically significantly different from zero. As $b$ approaches $\infty$ the standard error of your estimator will tend towards zero. This allows you to study the sampling distribution of your estimators in your actual sample size.Īs notes, you can arbitrarily increase your bootstrapped sample size (lets call it $b$). The size of each bootstrapped sample should be the same as your actual sample size (i.e., n=50). Bootstrapping typically involves sampling with replacement from your sample data.
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