I’ve been blogging a lot lately about plagiarism (sorry, Bob!), and one thing that’s been bugging me is, why does it bother me so much. Part of the story is simple: much of my reputation comes from the words I write, so I bristle at any attempt to devalue words. I feel the same way [...]The post To Throw Away Data: Plagiarism as a Statistical Crime appeared first on Statistical…
Lee Sechrest sends along this article by Brian Haig and writes that it “presents what seems to me a useful perspective on much of what scientists/statisticians do and how science works, at least in the fields in which I work.” Here’s Haig’s abstract: A broad theory of scientific method is sketched that has particular relevance [...]The post Where do theories come from? appeared first on Statistical Modeling, Causal Inference, and…
I received two emails yesterday on related topics. First, Stephen Olivier pointed me to this post by Daniel Lakens, who wrote the following open call to statisticians: You would think that if you are passionate about statistics, then you want to help people to calculate them correctly in any way you can. . . . [...]The post How can statisticians help psychologists do their research better? appeared first on Statistical…
I was asked to write an article for the Committee of Presidents of Statistical Societies (COPSS) 50th anniversary volume. Here it is (it’s labeled as “Chapter 1,” which isn’t right; that’s just what came out when I used the template that was supplied). The article begins as follows: The field of statistics continues to be [...]The post How do we choose our default methods? appeared first on Statistical Modeling, Causal…
Several months ago, Mike Betancourt and I wrote a discussion for the article, Can quantum probability provide a new direction for cognitive modeling?, by Emmanuel Pothos and Jerome Busemeyer, in Behavioral and Brain Sciences. We didn’t say much, but it was a milestone for me because, with this article, BBS became the 100th journal I’d [...]The post Does quantum uncertainty have a place in everyday applied statistics? appeared first on…
Miguel Paz writes: Poderomedia Foundation and PinLatam are launching OpenDataLatinoamerica.org, a regional data repository to free data and use it on Hackathons and other activities by HacksHackers chapters and other organizations. We are doing this because the road to the future of news has been littered with lost datasets. A day or so after every [...]The post OpenData Latinoamerica appeared first on Statistical Modeling, Causal Inference, and Social Science.
In an email I sent to a colleague who’s writing about lasso and Bayesian regression for R users: The one thing you might want to add, to fit with your pragmatic perspective, is to point out that these different methods are optimal under different assumptions about the data. However, these assumptions are never true (even [...]The post Same old same old appeared first on Statistical Modeling, Causal Inference, and Social…
Rachel Schutt writes: The hype surrounding Big Data and Data Science is at a fever pitch with promises to solve the world’s business and social problems, large and small. How accurate or misleading is this message? How is it helping or damaging people, and which people? What opportunities exist for data nerds and entrepreneurs that [...]The post NYC Data Skeptics Meetup appeared first on Statistical Modeling, Causal Inference, and Social…
Betsey Stevenson and Justin Wolfers have been inspired by the recent Reinhardt and Rogoff debacle to list “six ways to separate lies from statistics” in economics research: 1. “Focus on how robust a finding is, meaning that different ways of looking at the evidence point to the same conclusion.” 2. Don’t confuse statistical with practical [...]The post 7 ways to separate errors from statistics appeared first on Statistical Modeling, Causal…
I’ve earlier written that science is science communication—that is, the act of communicating scientific ideas and findings to ourselves and others is itself a central part of science. My point was to push against a conventional separation between the act of science and the act of communication, the idea that science is done by scientists [...]The post “Tragedy of the science-communication commons” appeared first on Statistical Modeling, Causal Inference, and…
