Introduction bootstrap and randomization test

Resampling means that the original dataset is used to generate new samples, the results of which can be analyzed. Bootstrap and randomization are two examples of resampling methods.
Bootstrap is used to estimate confidence intervals
Randomization is used to perform tests

To bootstrap means lots of samples drawn with replacement from the original data. The sample size is the size of the original data.
The idea behind the bootstrap is that the real population is the best approximated by a population that consists of infinitely many copies of the original sample. This comes down to draw a new sample from the original sample with replacement.

In the figure below you can see the number of times an object is chosen, shown by the width of the border. Each sample provides a new average or median.

The bootstrap will be used to determine the confidence interval for a mean, proportion, for difference for two averages, for difference of two proportions, and for the slope regression, bootstrap is a very general method, applicable in many situations.

A test is about a research question. An example of research question is: are there real differences between two groups or can be the difference explained by chance? Membership of the group is than the explanatory variable. If the assumption is true that the membership has no influence on the observed random variable, the allocation to the group. The randomization test does this random allocation a large number of times and that way, you can gain insight into the expected deviation.

For many situations are also traditional tests such as the t-test available. The advantage of this resampling methods from a pedagogical point of view, that it is not necessary to discuss in detail a number of concepts such as distribution before using the concepts confidence interval and tests. The methods are quite robust.

Extra information
On the web is a lot of extra information available. There are many excellent articles published regarding teaching on simulations-based conclusions. on the blog:

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