Bayesian statistics is becoming a popular approach to handling complex statistical modeling. This special issue of Evaluation Review features several Bayesian contributions. In this overview, I present the basics of Bayesian inference. Bayesian statistics is based on the principle that parameters have a distribution of beliefs about them that behave exactly like probability distributions. We can use Bayes’ Theorem to update our beliefs about values of the parameters as new information becomes available. Even better, we can make statements that frequentists do not, such as “the probability that an effect is larger than 0 is .93,” and can interpret 95% (e.g.) intervals as people naturally want, that there is a 95% probability that the parameter is in that interval. I illustrate the basic concepts of Bayesian statistics through a simple example of predicting admissions to a PhD program.

贝叶斯统计正在成为处理复杂统计建模的流行方法。本期《评价评论》特刊具有几个贝叶斯贡献。在本概述中，我将介绍贝叶斯推理的基础知识。贝叶斯统计基于这样一个原理，即参数具有关于它们的信念分布，其行为与概率分布完全相同。当新信息可用时，我们可以使用贝叶斯定理来更新我们对参数值的看法。更好的是，我们可以做出常客不会的陈述，例如“效应大于 0 的概率为 0.93”，并且可以按照人们自然想要的方式解释 95%（例如）区间，即有 95% 的概率参数在那个区间内。我通过一个预测博士项目录取的简单例子来说明贝叶斯统计的基本概念。