Time Inc. stoops to the level of the American Society of Human Genetics and PPNAS?

Do anyone out there know anyone at Time Inc? If so, I have a question for you. But first the story: Mark Palko linked to an item from Barry Petchesky pointing out this article at the online site of Sports Illustrated Magazine. Here’s Petchesky: Over at Sports Illustrated, you can read an article about Tom Brady’s new line of sleepwear for A Company That Makes Stretchy Workout Stuff. The article contains the following lines: “The TB12 Sleepwear line includes full-length shirts and pants—and a short-sleeve and shorts version—with bioceramics printed on the inside.” “The print, sourced from natural minerals, activates the body’s natural heat and reflects it back as far infrared energy…” “The line, available in both men’s [link to store for purchase] and women’s [link to store for purchase] sizes, costs between $80 to $100 [link to store for…
Original Post: Time Inc. stoops to the level of the American Society of Human Genetics and PPNAS?

We fiddle while Rome burns: p-value edition

Raghu Parthasarathy presents a wonderfully clear example of disastrous p-value-based reasoning that he saw in a conference presentation. Here’s Raghu: Consider, for example, some tumorous cells that we can treat with drugs 1 and 2, either alone or in combination. We can make measurements of growth under our various drug treatment conditions. Suppose our measurements give us the following graph: . . . from which we tell the following story: When administered on their own, drugs 1 and 2 are ineffective — tumor growth isn’t statistically different than the control cells (p > 0.05, 2 sample t-test). However, when the drugs are administered together, they clearly affect the cancer (p < 0.05); in fact, the p-value is very small (0.002!). This indicates a clear synergy between the two drugs: together they have a much stronger effect than each alone does.…
Original Post: We fiddle while Rome burns: p-value edition

“Which curve fitting model should I use?”

Oswaldo Melo writes: I have learned many of curve fitting models in the past, including their technical and mathematical details. Now I have been working on real-world problems and I face a great shortcoming: which method to use. As an example, I have to predict the demand of a product. I have a time series collected over the last 8 years. A simple set of (x,y) data about the relationship between the demand of a product on a certain week. I have this for 9 products. And to continue the study, I must predict the demand of each product for the next years. Looks easy enough, right? Since I do not have the probability distribution of the data, just use a non-parametric curve fitting algorithm. But which one? Kernel smoothing? B-splines? Wavelets? Symbolic regression? What about Fourier analysis? Neural networks?…
Original Post: “Which curve fitting model should I use?”

Two unrelated topics in one post: (1) Teaching useful algebra classes, and (2) doing more careful psychological measurements

Kevin Lewis and Paul Alper send me so much material, I think they need their own blogs. In the meantime, I keep posting the stuff they send me, as part of my desperate effort to empty my inbox. 1. From Lewis: “Should Students Assessed as Needing Remedial Mathematics Take College-Level Quantitative Courses Instead? A Randomized Controlled Trial,” by A. W. Logue, Mari Watanabe-Rose, and Daniel Douglas, which begins: Many college students never take, or do not pass, required remedial mathematics courses theorized to increase college-level performance. Some colleges and states are therefore instituting policies allowing students to take college-level courses without first taking remedial courses. However, no experiments have compared the effectiveness of these approaches, and other data are mixed. We randomly assigned 907 students to (a) remedial elementary algebra, (b) that course with workshops, or (c) college-level statistics with…
Original Post: Two unrelated topics in one post: (1) Teaching useful algebra classes, and (2) doing more careful psychological measurements

Sethi on Schelling

Rahul says: I loved Schelling’s writing. His thought experiments on negotiation gave such fascinating insights even to a total layman like me. Personally, I think that’s one hallmark of a great writer / thinker: To produce work whose greatness is evident even to a virtual novice to the field without needing a mastery of the field to appreciate.
Original Post: Sethi on Schelling

“Dirty Money: The Role of Moral History in Economic Judgments”

“Dirty Money: The Role of Moral History in Economic Judgments” Posted by Andrew on 23 December 2016, 9:57 am Recently in the sister blog . . . Arber Tasimi and his coauthor write: Although traditional economic models posit that money is fungible, psychological research abounds with examples that deviate from this assumption. Across eight experiments, we provide evidence that people construe physical currency as carrying traces of its moral history. In Experiments 1 and 2, people report being less likely to want money with negative moral history (i.e., stolen money). Experiments 3–5 provide evidence against an alternative account that people’s judgments merely reflect beliefs about the consequences of accepting stolen money rather than moral sensitivity. Experiment 6 examines whether an aversion to stolen money may reflect con- tamination concerns, and Experiment 7 indicates that people report they would donate stolen money,…
Original Post: “Dirty Money: The Role of Moral History in Economic Judgments”

Steve Fienberg

I did not know Steve Fienberg well, but I met him several times and encountered his work on various occasions, which makes sense considering his research area was statistical modeling as applied to social science. Fienberg’s most influential work must have been his books on the analysis of categorical data, work that was ahead of its time in being focused on the connection between models rather than hypothesis tests. He also wrote, with William Mason, the definitive paper on identification in age-period-cohort models, and he worked on lots of applied problems including census adjustment, disclosure limitation, and statistics in legal settings. The common theme in all this work is the combination of information from multiple sources, and the challenges involved in taking statistical inferences using these to make decisions in new settings. These ideas of integration and partial pooling are…
Original Post: Steve Fienberg

On deck very soon

On deck very soon Posted by Andrew on 19 December 2016, 10:52 pm A bunch of the 170 are still in the queue. I haven’t been adding to the scheduled posts for awhile, instead I’ve been inserting topical items from time to time—I even got some vicious hate mail for my article on the electoral college—and then I’ve been shoving material for new posts into a big file that now has a couple hundred items, I’m not quite sure what to do with that one, maybe I’ll write all my posts for 2017 on a single day and get that over with? Also, sometimes our co-bloggers post here, and that’s cool. Anyway, three people emailed me today about a much-publicized science news item that pissed them off. It’s not really topical but maybe I’ll post on it, just to air the…
Original Post: On deck very soon

An efficiency argument for post-publication review

An efficiency argument for post-publication review Posted by Andrew on 16 December 2016, 9:59 am This came up in a discussion last week: We were talking about problems with the review process in scientific journals, and a commenter suggested that prepublication review should be more rigorous: There are lot of statistical missteps you just can’t catch until you actually have the replication data in front of you to work with and look at. Andrew, do you think we will ever see a system implemented where you have to submit the replication code with the initial submission of the paper, rather than only upon publication (or not at all)? If reviewers had the replication files, they could catch many more of these types of arbitrary specification and fishing problems that produce erroneous results, saving the journal from the need for a correction.…
Original Post: An efficiency argument for post-publication review

Hark, hark! the p-value at heaven’s gate sings

Three different people pointed me to this post, in which food researcher and business school professor Brian Wansink advises Ph.D. students to “never say no”: When a research idea comes up, check it out, put some time into it and you might get some success. I like that advice and I agree with it. Or, at least, this approached worked for me when I was a student and it continues to work for me now, and my favorite students are those who follow this approach. That said, there could be some selection bias here, that the students who say Yes to new projects are the ones who are more likely to be able to make use of such opportunities. Maybe the students who say No would just end up getting distracted and making no progress, were they to follow this…
Original Post: Hark, hark! the p-value at heaven’s gate sings