Scott Alexander criticizes the misleading use of 'no evidence' in science communication and suggests more nuanced alternatives. Longer summary

The post critiques the use of the phrase 'no evidence' in science communication, arguing that it's misleading and erodes public trust. Scott Alexander shows how the phrase is used inconsistently to mean both 'plausible but not yet proven' and 'definitively false'. He explains that this stems from a misunderstanding of how real truth-seeking works, which should be Bayesian rather than based on a simplistic null hypothesis model. The post concludes by suggesting better ways for journalists to communicate scientific uncertainty, including being more specific about the state of evidence and engaging with the arguments of those who believe differently. Shorter summary

The review examines Nicholson Baker's 'Double Fold', a critique of libraries replacing original books and newspapers with inferior microfilm copies. Longer summary

This review discusses Nicholson Baker's book 'Double Fold', which criticizes the widespread practice of American libraries discarding original books and newspapers in favor of microfilm copies. The review outlines Baker's arguments against this practice, including the loss of historical artifacts, the poor quality of microfilm, and the flawed science behind claims of paper deterioration. It also touches on the responses to Baker's book and reflects on its relevance 20 years after publication. Shorter summary

Scott examines how the 5-HTTLPR gene was wrongly linked to depression for years, discussing the implications for psychiatric research and genetic testing products. Longer summary

This post discusses the history and eventual debunking of the 5-HTTLPR gene's supposed role in depression. Initially, numerous studies claimed to find links between 5-HTTLPR and various mental health conditions, interactions with stress, and even antidepressant efficacy. However, a recent large-scale study by Border et al. found no evidence for these claims, suggesting that hundreds of previous studies were likely false positives. The post explores the implications of this finding, including concerns about the reliability of psychiatric research, the tendency for studies to reinforce existing beliefs, and the questionable validity of pharmacogenomic testing products that rely on genes like 5-HTTLPR. Shorter summary

Scott Alexander criticizes Ted Chiang's article that compares AI risk to capitalism, arguing that the analogy is flawed and the reasoning behind it is unsound. Longer summary

Scott Alexander critiques Ted Chiang's article comparing AI risk to capitalism, arguing that the comparison is flawed and the reasoning unsound. He points out that AI risk concerns originated from academics, not just Silicon Valley, and that drawing analogies between scientific concepts and social phenomena doesn't disprove the original concept. Scott also criticizes Chiang's use of psychological projection to explain AI fears, noting the dangers of amateur psychoanalysis. He concludes by emphasizing that this approach to risk assessment is inappropriate for potentially catastrophic issues. Shorter summary

Scott Alexander criticizes websites that misleadingly suggest drug side effects by scraping FDA data, potentially causing patients to stop taking necessary medications. Longer summary

Scott Alexander criticizes websites like EHealthMe that automatically generate pages suggesting connections between drugs and side effects based on FDA data scraping. He argues these sites are misleading and potentially harmful, as they can cause patients to stop taking necessary medications due to unfounded fears of side effects. The post begins with a personal anecdote about a patient concerned about Xolair causing depression, then delves into how these websites operate and why their information is unreliable. Scott emphasizes the scummy nature of these practices and their potential to harm vulnerable individuals, concluding with a stark example of how such misinformation could lead to tragedy. Shorter summary

Scott Alexander dissects and criticizes Breitbart's misleading use of statistics about illegal immigrant crime, emphasizing the need for better understanding and interpretation of data in political debates. Longer summary

Scott Alexander critiques a Breitbart article about illegal immigrant crime statistics, pointing out numerous flaws in their reasoning and use of data. He highlights how the article misinterprets or misrepresents statistics, fails to provide necessary context, and makes illogical conclusions. Scott argues that while there may be legitimate concerns about illegal immigration, the article's approach is misleading and unhelpful. He concludes by emphasizing the importance of recognizing flawed arguments on both sides of political debates, rather than assuming one's opponents are simply gullible. Shorter summary

Scott debunks viral misinformation about false rape accusation rates and provides more accurate estimates, while criticizing the spread of such inaccuracies in feminist circles. Longer summary

Scott Alexander critiques a viral Buzzfeed article that claims false rape accusations are extremely rare, showing how the article's statistics are severely flawed. He provides a more accurate analysis of false rape accusation rates, estimating they affect between 0.3% to 3% of men in their lifetimes. Scott expresses frustration at how readily such misinformation spreads in feminist circles and urges readers to be extremely skeptical of statistics from these sources. He concludes by discussing the difficulty of dealing with rape accusations given the significant rates of both rape and false accusations. Shorter summary

Scott Alexander reviews two papers exposing statistical manipulation techniques in psychology research and addiction treatment program evaluations. Longer summary

This post discusses two papers on statistical manipulation in scientific studies. The first paper, 'False Positive Psychology', demonstrates how researchers can use four tricks to artificially achieve statistical significance: measuring multiple dependent variables, choosing when to end experiments, controlling for confounders, and testing different conditions. The authors show these tricks can make random data appear significant 61% of the time. The second paper, 'How To Have A High Success Rate In Treatment', reveals how addiction treatment programs can inflate their success rates through various methods like carefully choosing the denominator, selecting promising candidates, redefining success, and omitting control groups. Both papers highlight the ease of manipulating statistics to produce desired results in research and treatment evaluations. Shorter summary