Statistical thinking is essential to understanding the nature of scientific results as a consumer. Statistical thinking also facilitates thinking like a scientist. Instead of emphasizing a “correct” procedure for data analysis and its outcome, statistical thinking focuses on the process of data analysis. This article reviews frequentist and Bayesian approaches such that teachers can promote less well-known statistical perspectives to encourage statistical thinking. Within the frequentist and Bayesian approaches, we highlight important distinctions between statistical evaluation versus estimation using an example on the facial feedback hypothesis. We first introduce some elementary statistical concepts, which are then illustrated with simulated data. Finally, we demonstrate how these approaches are applied to empirical data obtained from a Registered Replication Report. Data and R code for the example are provided as supplementary teaching material. We conclude with a discussion of key learning outcomes centred on promoting statistical thinking.

中文翻译：

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数据分析的频率论和贝叶斯方法：评估和估计

作为消费者，统计思维对于理解科学结果的性质至关重要。统计思维也有助于像科学家一样思考。统计思维不是强调数据分析及其结果的“正确”程序，而是侧重于数据分析的过程。本文回顾了频率论和贝叶斯方法，以便教师可以推广鲜为人知的统计观点，以鼓励统计思维。在频率论和贝叶斯方法中，我们使用面部反馈假设的示例强调统计评估与估计之间的重要区别。我们首先介绍一些基本的统计概念，然后用模拟数据进行说明。最后，我们展示了如何将这些方法应用于从注册复制报告中获得的经验数据。示例的数据和 R 代码作为补充教材提供。我们最后讨论了以促进统计思维为中心的关键学习成果。