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This chapter evaluates Bayesian inference, which refers to the Bayesian statistical method for estimating the parameters of a model and for testing a hypothesis. It relies on subjective statistics and extensively uses Bayes’s theorem. In the early 1990s, Bayesian statistics boomed with the emergence of sampling techniques. These new tools rely on the computational power to sample from (rather than evaluate) the posterior probability. However, the main drawback of the Bayesian approach lies in the computation of the posterior probability. The analytical computation of the posterior probability is a complex problem for any application, and this has limited Bayesian statistics for years.

This chapter assesses sampling techniques. Researchers may restrict their data collection to a sample of a population for convenience or necessity if they lack the time and resources to collect data for the entire population. Therefore, a sample is any subset of units collected from a population. Research sampling techniques refer to case selection strategy — the process and methods used to select a subset of units from a population. While sampling techniques reduce the costs of data collection, they induce a loss in terms of comprehensiveness and accuracy, compared to working on the entire population. The data collected are subject to errors or bias. Two main decisions determine the size or margin of error and whether the results of a sample study can be generalized and applied to the entire population with accuracy: the choice of sample type and the sample size.