Statistical evidence is fundamental to science. Understanding statistics helps us to understand the literature and assess it critically, refine our research questions into testable hypotheses and predictions, design studies that are appropriate to test these predictions, evaluate whether our findings support our predictions, and derive appropriate conclusions. The dominant paradigm in primatology and allied disciplines is to test whether patterns we observe in our observations are due to more than random variation in our data. However, the statistical analyses we use to do this are very often misinterpreted. In this chapter I distinguish different kinds of variables, then introduce relationships between variables. I explain how we use statistical analysis to infer something about a theoretical population based on a sample. I introduce null hypothesis significance testing and explain common misunderstandings of this approach. I review the two types of error that arise in NHST and the concept of statistical power. I explain the need to assess and report effect sizes and confidence intervals, briefly introduce alternatives to null hypothesis statistical testing and end with how to interpret statistical results appropriately.