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Archive of posts filed under the Zombies category.

The p-value is 4.76×10^−264 1 in a quadrillion

Ethan Steinberg writes: It might be useful for you to cover the hilariously bad use of statistics used in the latest Texas election lawsuit. Here is the raw source, with the statistics starting on page 22 under the heading “Z-Scores For Georgia”. . . . The main thing about this analysis that’s so funny is […]

Unlike MIT, Scientific American does the right thing and flags an inaccurate and irresponsible article that they mistakenly published. Here’s the story:

Scene 1 A few months ago I wrote about a really bad article that appeared in Undark, MIT’s science magazine. The article was so bad it lowered my opinion of MIT, my alma mater, in that it showed such poor judgment by the administration to sponsor this kind of irresponsible anti-scientific crap. MIT’s done worse […]

From the Archives of Psychological Science

Jay Livingston pointed me to PostSecret, which I’d never heard of before, and the above image, which apparently first appeared in 2011. P.S. The image and the title of this post do not quite align. My concern with the journal Psychological Science is about incompetent work rather than made-up data.

Unfair to James Watson?

A reader writes: I usually enjoy your blog, but when I saw the first sentence of this recent post it left a bad taste that just didn’t go away. The post in question was titled, “New England Journal of Medicine engages in typical academic corporate ass-covering behavior,” and its first sentence began, “James Watson (not […]

Bishops of the Holy Church of Embodied Cognition and editors of the Proceedings of the National Academy of Christ

Paul Alper points to a recent New York Times article about astrology as a sign that the world is going to hell in a handbasket. My reply: Astrology don’t bug me so much cos it doesn’t pretend to be science. I’m more bothered by PNAS-style fake science because it pretends to be real science. Same […]

A new hot hand paradox

1. Effect sizes of just about everything are overestimated. Selection on statistical significance, motivation to find big effects to support favorite theories, researcher degrees of freedom, looking under the lamp-post, and various other biases. The Edlin factor is usually less than 1. (See here for a recent example.) 2. For the hot hand, it’s the […]

The 200-year-old mentor

Carl Reiner died just this year and Mel Brooks is, amazingly, still alive. But in any case their torch will be carried forward, as long as there are social scientists who are not in full control of their data. The background is the much-discussed paper, “The association between early career informal mentorship in academic collaborations […]

Today in spam

1. From “William Jessup,” subject line “Invitation: Would you like to join GlobalWonks?”: Dear Richard, I wanted to follow up one last time about my invitation to join our expert-network. We are happy to compensate you for up to $900 per hour for our client engagements. If you would like to join us, you may […]

Authors repeat same error in 2019 that they acknowledged and admitted was wrong in 2015

David Allison points to this story: Kobel et al. (2019) report results of a cluster randomized trial examining the effectiveness of the “Join the Healthy Boat” kindergarten intervention on BMI percentile, physical activity, and several exploratory outcomes. The authors pre-registered their study and described the outcomes and analysis plan in detail previously, which are to […]

No, I don’t believe etc etc., even though they did a bunch of robustness checks.

Dale Lehman writes: You may have noticed this article mentioned on Marginal Revolution, I [Lehman] don’t have access to the published piece, but here’s a working paper version. It might be worth your taking a look. It has all the usual culprits: forking paths, statistical significance as the filter, etc etc. As usual, it […]

How science and science communication really work: coronavirus edition

Now that the election’s over, we can return to our regular coronavirus coverage. Nothing new since last night, so I wanted to share a couple of posts from a few months ago that I think remain relevant: No, there is no “tension between getting it fast and getting it right”: On first hearing, this statement […]

Post-election post

A favorite demonstration in statistics classes is to show a coin and ask what is the probability it comes up heads when flipped. Students will correctly reply 1/2. You then flip the coin high into the air, catch it, slap it on your wrist, look at it, and cover it up again with your hand. […]

Sh*ttin brix in the tail…

After my conversation with Andrew yesterday about The Economist election forecasting model I got curious about how G. Elliot, Merlin and Andrew want their prediction to be assessed given the menu of strange contingencies we have in front of us. I checked Betfair rules for some guidance: This market will be settled according to the candidate […]

So, what’s with that claim that Biden has a 96% chance of winning? (some thoughts with Josh Miller)

As indicated above, our model gives Joe Biden a 99+% chance of receiving more votes than Donald Trump and a 96% chance of winning in the electoral college. Michael Wiebe wrote in to ask: Your Economist model currently says that Biden has a 96% chance of winning the electoral college. How should we think about […]

Stephen Wolfram invented a time machine but has been too busy to tell us about it

Following up on our previous posts here and here . . . I came across this interview of John Conway and Siobhan Roberts, Conway’s biographer: Siobhan, from the book I felt that there was a strong sense of competitiveness and ego in the maths world. In the research that you did, and conversations that you […]

Reverse-engineering the problematic tail behavior of the Fivethirtyeight presidential election forecast

We’ve been writing a bit about some odd tail behavior in the Fivethirtyeight election forecast, for example that it was giving Joe Biden a 3% chance of winning Alabama (which seemed high), it was displaying Trump winning California as in “the range of scenarios our model thinks is possible” (which didn’t seem right), and it […]

An odds ratio of 30, which they (sensibly) don’t believe

Florian Wickelmaier and Katharina Naumann write: In a lab course, we came across a study on the influence of “hemispheric activation” on the framing effect in decision making by Todd McElroy and John J. Seta [Brain and Cognition 55 (2004) 572-580, doi:10.1016/j.bandc.2004.04.002]: Two experiments were conducted to determine whether the functional specializations of the left […]

Piranhas in the rain: Why instrumental variables are not as clean as you might have thought

Woke up in my clothes again this morning I don’t know exactly where I am And I should heed my doctor’s warning He does the best with me he can He claims I suffer from delusion But I’m so confident I’m sane It can’t be a statistical illusion So how can you explain Piranhas in […]

Estimated “house effects” (biases of pre-election surveys from different pollsters) and here’s why you have to be careful not to overinterpret them:

Elliott provides the above estimates from our model. As we’ve discussed, as part of our fitting procedure we estimate various biases, capturing in different ways the fact that surveys are not actually random samples of voters from an “urn.” One of these biases is the “house effect.” In our model, everything’s on the logit scale, […]

Whassup with the dots on our graph?

The above is from our Economist election forecast. Someone pointed to me that our estimate is lower than all the dots in October. Why is that? I can come up with some guesses, but it’s surprising that the line is below all the dots. Merlin replied: That happened a bunch of times before as well. […]