Regardless of the shape of the bootstrap sampling distribution, we can use the percentile method to construct a confidence interval. Using this method, the 95% confidence interval is the range of points that cover the middle 95% of bootstrap sampling distribution. The following examples use StatKey.

To construct a 95% bootstrap confidence interval using the percentile method follow these steps:

1. Determine what type(s) of variable(s) you have and what parameters you want to estimate. StatKey will bootstrap a confidence interval for a mean, median, standard deviation, proportion, different in two means, difference in two proportions, regression slope, and correlation (Pearson's r). 
2. Get your sample data into StatKey.  There are some built-in datasets and you always have the ability to enter in your own data. This procedure varies depending on the test you're conducting.  For a proportion, you need to enter the number of successes and number of trials.  For anything involving quantitative data you will need to copy and paste your data into StatKey (this is the recommended method) or upload it as a txt, csv, or tsv file. 
3. Generate at least 5,000 bootstrap samples.
4. Check the "Two-Tail" box at the upper left corner of the bootstrap dotplot. By default, this will give you a 95% confidence interval.

The default in StatKey is to construct a 95% confidence interval. You can change the confidence level by clicking the "0.950" in the center and entering the confidence level you want. For example, for a 90% confidence interval you would enter "0.90." Below is a short video demonstrating this.