We begin with a graphical approach to bootstrapping and permutation testing, illuminating basic statistical concepts of standard errors, confidence intervals, p-values and significance tests. We consider a variety of statistics (mean, trimmed mean, regression, etc.), and a number of sampling situations (one-sample, two-sample, stratified, finite-population), stressing the common techniques that apply in these situations. We’ll look at applications from a variety of fields, including telecommunications, finance, and biopharm. These methods let us do confidence intervals and hypothesis tests when formulas are not available, so we can do better statistics, e.g. use robust methods like medians, trimmed means, or robust regression. They can help clients understand statistical variability. And some of the methods are more accurate than standard methods.