Points of Significance is an ongoing series of short articles about statistics in Nature Methods that started in 2013. Its aim is to provide clear explanations of essential concepts in statistics for a nonspecialist audience. The articles favor heuristic explanations and make extensive use of simulated examples and graphical explanations, while maintaining mathematical rigor. Topics range from basic, but often misunderstood, such as uncertainty and p-values, to relatively advanced, but often neglected, such as the error-in-variables problem and the curse of dimensionality. More recent articles have focused on timely topics such as modeling of epidemics, machine learning, and neural networks. In this article, we discuss the evolution of topics and details behind some of the story arcs, our approach to crafting statistical explanations and narratives, and our use of figures and numerical simulations as props for building understanding.

中文翻译：

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制作 10 年统计解释：重要点


Points of Significance 是 Nature Methods 中正在进行的一系列关于统计学的短文，始于 2013 年。其目的是为非专业受众提供统计学中基本概念的清晰解释。这些文章偏爱启发式解释，并广泛使用模拟示例和图形解释，同时保持数学的严谨性。主题范围从基本但经常被误解的（如不确定性和 p 值）到相对高级但经常被忽视的（如变量误差问题和维度的诅咒）。最近的文章侧重于流行病建模、机器学习和神经网络等及时的主题。在本文中，我们将讨论一些故事情节背后的主题和细节的演变，我们制作统计解释和叙述的方法，以及我们使用数字和数值模拟作为建立理解的道具。