andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1276 knowledge-graph by maker-knowledge-mining
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Introduction: Rick Lightburn writes: I [Lightburn] am also a member of the group Business Analytics on LinkedIn. I am struck by what I perceive as the gross misuse of statistics by the members of this group, including things that (I thought) were taught in Introductory Statistics courses in business schools. I want to suggest to you that you look at the discussions there if you want examples of such abuse. The discussions there support me in my belief that Analytics is data manipulation in the support of previously developed conclusions. My reply: I don’t think it’s such a bad thing. I like when people make statistical arguments, even bad statistical arguments. Once you accept the concept of arguing from logic and data, maybe you’ll be open to learning something new
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1 Rick Lightburn writes: I [Lightburn] am also a member of the group Business Analytics on LinkedIn. [sent-1, score-0.328]
2 I am struck by what I perceive as the gross misuse of statistics by the members of this group, including things that (I thought) were taught in Introductory Statistics courses in business schools. [sent-2, score-1.462]
3 I want to suggest to you that you look at the discussions there if you want examples of such abuse. [sent-3, score-0.635]
4 The discussions there support me in my belief that Analytics is data manipulation in the support of previously developed conclusions. [sent-4, score-1.158]
5 My reply: I don’t think it’s such a bad thing. [sent-5, score-0.159]
6 I like when people make statistical arguments, even bad statistical arguments. [sent-6, score-0.437]
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same-blog 1 1.0000001 1276 andrew gelman stats-2012-04-22-“Gross misuse of statistics” can be a good thing, if it indicates the acceptance of the importance of statistical reasoning
Introduction: Rick Lightburn writes: I [Lightburn] am also a member of the group Business Analytics on LinkedIn. I am struck by what I perceive as the gross misuse of statistics by the members of this group, including things that (I thought) were taught in Introductory Statistics courses in business schools. I want to suggest to you that you look at the discussions there if you want examples of such abuse. The discussions there support me in my belief that Analytics is data manipulation in the support of previously developed conclusions. My reply: I don’t think it’s such a bad thing. I like when people make statistical arguments, even bad statistical arguments. Once you accept the concept of arguing from logic and data, maybe you’ll be open to learning something new
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Introduction: David Shor sends along a job announcement for Civis Analytics, which he describes as “basically Obama’s Analytics team reconstituted as a company”: Data Scientist Position Overview Data Scientists are responsible for providing the fundamental data science that powers our work – including predictive analytics, data mining, experimental design and ad-hoc statistical analysis. As a Data Scientist, you will join our Chicago-based data science team, working closely and collaboratively with analysts and engineers to identify, quantify and solve big, meaningful problems. Data Scientists will have the opportunity to dive deeply into big problems and work in a variety of areas. Civis Analytics has opportunities for applicants who are seasoned professionals, brilliant new comers, and anywhere in between. Qualifications · Master’s degree in statistics, machine learning, computer science with heavy quant focus, a related subject, or a Bachelor’s degree and significant work ex
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Introduction: Cool! I know Bayes has reached the big time when I receive spam like this: Bayesian networks are rapidly emerging as a new research paradigm . . . With this monthly newsletter, we’ll keep you up to date . . . Financial Analytics Webinar . . . will exhibit at this year’s INFORMS Analytics Conference in downtown Chicago. Please join us for our Bayesian networks technology workshop on April 10 . . . a powerful desktop application (Windows/Mac/Unix) for knowledge discovery, data mining, analytics, predictive modeling and simulation . . . the world’s only comprehensive software package for learning, editing and analyzing Bayesian networks . . . If you no longer wish to receive these emails, please reply to this message with “Unsubscribe” in the subject line . . . You know the saying, “It’s not real unless it’s on TV”? My saying is: It’s not real until it’s on spam.
4 0.11118143 465 andrew gelman stats-2010-12-13-$3M health care prediction challenge
Introduction: i received the following press release from the Heritage Provider Network, “the largest limited Knox-Keene licensed managed care organization in California.” I have no idea what this means, but I assume it’s some sort of HMO. In any case, this looks like it could be interesting: Participants in the Health Prize challenge will be given a data set comprised of the de-identified medical records of 100,000 individuals who are members of HPN. The teams will then need to predict the hospitalization of a set percentage of those members who went to the hospital during the year following the start date, and do so with a defined accuracy rate. The winners will receive the $3 million prize. . . . the contest is designed to spur involvement by others involved in analytics, such as those involved in data mining and predictive modeling who may not currently be working in health care. “We believe that doing so will bring innovative thinking to health analytics and may allow us to solve at
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Introduction: I just finished reading this book by Kevin Cook. Nothing surprising, but it’s got almost all the stories, including many that I’d never previously read. Excellent if you like that sort of thing. It’s just too bad Thompson wasn’t around to golf-hustle Michael Jordan, back when that was big business.
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Introduction: Rick Lightburn writes: I [Lightburn] am also a member of the group Business Analytics on LinkedIn. I am struck by what I perceive as the gross misuse of statistics by the members of this group, including things that (I thought) were taught in Introductory Statistics courses in business schools. I want to suggest to you that you look at the discussions there if you want examples of such abuse. The discussions there support me in my belief that Analytics is data manipulation in the support of previously developed conclusions. My reply: I don’t think it’s such a bad thing. I like when people make statistical arguments, even bad statistical arguments. Once you accept the concept of arguing from logic and data, maybe you’ll be open to learning something new
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Introduction: 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 examine the larger issues with a skeptical view? This Meetup focuses on mathematical, ethical, and business aspects of data from a skeptical perspective. Guest speakers will discuss the misuse of and best practices with data, common mistakes people make with data and ways to avoid them, how to deal with intentional gaming and politics surrounding mathematical modeling, and taking into account the feedback loops and wider consequences of modeling. We will take deep dives into models in the fields of Data Science, statistics, financial engineering, and economics. This is an independent forum and open to anyone sharing an interest in the larger use of
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Introduction: David Shor sends along a job announcement for Civis Analytics, which he describes as “basically Obama’s Analytics team reconstituted as a company”: Data Scientist Position Overview Data Scientists are responsible for providing the fundamental data science that powers our work – including predictive analytics, data mining, experimental design and ad-hoc statistical analysis. As a Data Scientist, you will join our Chicago-based data science team, working closely and collaboratively with analysts and engineers to identify, quantify and solve big, meaningful problems. Data Scientists will have the opportunity to dive deeply into big problems and work in a variety of areas. Civis Analytics has opportunities for applicants who are seasoned professionals, brilliant new comers, and anywhere in between. Qualifications · Master’s degree in statistics, machine learning, computer science with heavy quant focus, a related subject, or a Bachelor’s degree and significant work ex
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Introduction: Rachel Schutt and Cathy O’Neil just came out with a wonderfully readable book on doing data science, based on a course Rachel taught last year at Columbia. Rachel is a former Ph.D. student of mine and so I’m inclined to have a positive view of her work; on the other hand, I did actually look at the book and I did find it readable! What do I claim is the least important part of data science? Here’s what Schutt and O’Neil say regarding the title: “Data science is not just a rebranding of statistics or machine learning but rather a field unto itself.” I agree. There’s so much that goes on with data that is about computing, not statistics. I do think it would be fair to consider statistics (which includes sampling, experimental design, and data collection as well as data analysis (which itself includes model building, visualization, and model checking as well as inference)) as a subset of data science. The question then arises: why do descriptions of data science focus so
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Introduction: Rajit Dasgupta writes: I have been working on a website, SlideRule that in its present state, is a catalog of online courses aggregated from over 35 providers. One of the products we are building on top of this is something called Learning Paths, which are essentially a sequence of Online Courses designed to help learners gain mastery over a certain subject. We have recently released a Learning Path on Data Analysis , contributed by Claudia Gold, an early data scientist at Airbnb. We’d love it if you could look at it and tell us what you think. We are always looking for constructive feedback. I clicked through and took a look. It’s pretty cool. I haven’t tried to assess the actual teaching materials (they’re mostly about programming, not statistics) but I like how it’s structured based on pointers to existing resources, which seems like an excellent compromise between (a) someone trying to write the material all himself or herself (which would require either limiting the sco
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Introduction: Since we’re talking about the scaled inverse Wishart . . . here’s a recent message from Chris Chatham: I have been reading your book on Bayesian Hierarchical/Multilevel Modeling but have been struggling a bit with deciding whether to model my multivariate normal distribution using the scaled inverse Wishart approach you advocate given the arguments at this blog post [entitled "Why an inverse-Wishart prior may not be such a good idea"]. My reply: We discuss this in our book. We know the inverse-Wishart has problems, that’s why we recommend the scaled inverse-Wishart, which is a more general class of models. Here ‘s an old blog post on the topic. And also of course there’s the description in our book. Chris pointed me to the following comment by Simon Barthelmé: Using the scaled inverse Wishart doesn’t change anything, the standard deviations of the invidual coefficients and their covariance are still dependent. My answer would be to use a prior that models the stan
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Introduction: Rick Lightburn writes: I [Lightburn] am also a member of the group Business Analytics on LinkedIn. I am struck by what I perceive as the gross misuse of statistics by the members of this group, including things that (I thought) were taught in Introductory Statistics courses in business schools. I want to suggest to you that you look at the discussions there if you want examples of such abuse. The discussions there support me in my belief that Analytics is data manipulation in the support of previously developed conclusions. My reply: I don’t think it’s such a bad thing. I like when people make statistical arguments, even bad statistical arguments. Once you accept the concept of arguing from logic and data, maybe you’ll be open to learning something new
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Introduction: We had 28 class periods, so I wrote an exam with an approximate correspondence of one question per class. Rather than dumping the exam in your lap all at once, I’ll post the questions once per day. Then each day I’ll post the answer to yesterday’s questions. So it will be 29 days in all. I’ll post them to appear late in the day so as not to interfere with our main daily posts (which are currently backed up to early June). The course was offered in the political science department and covered a mix of statistical and political topics. Followers of our recent discussion on test questions won’t be surprised to learn that some of the questions are ambiguous. This wasn’t on purpose. I tried my best, but good questions are hard to write. Question 1 will appear tomorrow.
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