Abstract This paper is a reader-friendly introduction to Bayesian inference applied to psychological science. We begin by explaining the difference between frequentist and epistemic interpretations of probability that underpin respectively frequentist and Bayesian statistics. We use a concrete example—a student wondering whether s/he carries the virus statisticus malignum—to explain how both approaches are different one from another. We illustrate Bayesian inference with intuitive examples, before introducing the mathematical framework. Different schools of thoughts and recommendations are discussed to illustrate how to use priors in Bayes Factor testing. We discuss how psychology could benefit from a greater reliance on Bayesian methods. Finally, we illustrate how to compute Bayes Factors analyses with real data and provide the R code.

中文翻译：

---

贝叶斯统计没有什么神奇之处：心理学初学者数据分析中的认知概率介绍

摘要 本文是对应用于心理科学的贝叶斯推理的读者友好介绍。我们首先解释频率论和概率的认知解释之间的差异，这些解释分别支持频率论和贝叶斯统计。我们用一个具体的例子——一个学生想知道他/她是否携带病毒统计数据——来解释这两种方法是如何不同的。在介绍数学框架之前，我们用直观的例子来说明贝叶斯推理。讨论了不同的思想和建议流派，以说明如何在贝叶斯因子测试中使用先验。我们讨论心理学如何从更多地依赖贝叶斯方法中受益。最后，我们说明了如何使用真实数据计算贝叶斯因子分析并提供 R 代码。