This chapter gives a brief, mostly nonmathematical, review of statistical hypothesis tests as used for making scientific inferences, with a focus on determining causality. We compare causation and association, reviewing the idea (popularized by Pearl) that probability models alone cannot determine causation, but other aspects of the study design are important for determining causality such as randomization. We differentiate between observational and experimental studies, with the latter much more suited to making causal inferences. We review the example of hormone replacement therapy and its relationship to heart disease in women, where earlier observational studies suggested that therapy reduced heart disease, while later, a large randomized trial showed the opposite. We discuss general issues with designing studies such as having a clear focus of the primary study question to avoid unintentionally exploring multiple hypotheses. We define and review validity, reliability, selection biases, placebo and Hawthorne effects, regression to the mean, blinding, dependence, intention-to-treat analyses, matching and inverse propensity score weighting.