ABSTRACT
Statistical inference is a form of inductive inference that employs probability to better understand the real-world phenomenon underlying a known data set. It allows scientists to formulate expectations about what they would observe in a new data set or in the larger population and to assess how confident they can be about those expectations. Normal distributions—unimodal and symmetric distributions—are especially important for statistical reasoning. Probability distributions are the engine of statistical inference. Using statistical inference to estimate features of a population from existing data about a sample is a powerful extension of statistical description. The similar step for statistical inference is called random sampling, where the individuals composing the sample are selected randomly from the population. The chapter describes the classical statistics method of hypothesis-testing is perhaps better when there is little background knowledge to draw upon or when scientists are unable to specify multiple alternative hypotheses.
