This chapter describes commonly used concepts for assessing statistical error-confidence intervals, p-values, and hypothesis tests. It outlines the logic underlying resampling methods. The chapter identifies common misinterpretations of computed quantities, and discusses some of the comparative advantages and disadvantages of using confidence intervals, p-values, classical hypothesis tests, and likelihood ratios for various purposes in forensic science. Along with idealized, simple examples of probabilistic processes, the chapter uses two principal examples from forensic science to illustrate the classical methods. The first involves an experiment to ascertain the validity and false positive probability of identifications made by latent fingerprint examiners. The second involves measurements of the refractive index of glass fragments. Confidence intervals are useful for presenting an estimate of the parameter value and for conveying a sense of the statistical error that is involved in making that estimate from the sample data.