Scott Alexander explores the difficulties in contextualizing statistics, providing numerous examples to show how the same data can be presented to seem significant or trivial. Longer summary

Scott Alexander discusses the challenges of putting statistical findings into context, showing how different comparisons can make the same statistic seem either significant or trivial. He provides numerous examples of effect sizes and correlations from various fields to illustrate this point. The post aims to promote awareness of how statistics can be manipulated and encourages readers to be vigilant when interpreting contextual comparisons. Scott also acknowledges the limitations of using standardized effect sizes but argues for their utility in certain situations where more specific measures are difficult to comprehend. Shorter summary

Scott Alexander shares a diverse collection of links and news items, covering topics from architecture and history to AI developments and scientific studies, with brief commentary on many items. Longer summary

This post is a collection of various links and news items curated by Scott Alexander. It covers a wide range of topics including architecture, history, animal welfare, optical illusions, scientific studies, AI developments, demographics, and more. Scott provides brief commentary on many of the items, sometimes expressing his personal opinions or highlighting interesting aspects. The post includes both light-hearted topics (like unusual baby names) and more serious discussions (such as AI safety concerns and research on gender bias in academia). Shorter summary