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Archive of posts filed under the Zombies category.

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, https://www.sciencedirect.com/science/article/abs/pii/S0167629619301237. 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. […]

Between-state correlations and weird conditional forecasts: the correlation depends on where you are in the distribution

Yup, here’s more on the topic, and this post won’t be the last, either . . . Jed Grabman writes: I was intrigued by the observations you made this summer about FiveThirtyEight’s handling of between-state correlations. I spent quite a bit of time looking into the topic and came to the following conclusions. In order […]

Calibration problem in tails of our election forecast

Following up on the last paragraph of this discussion, Elliott looked at the calibration of our state-level election forecasts, fitting our model retroactively to data from the 2008, 2012, and 2016 presidential elections. The plot above shows the point prediction and election outcome for the 50 states in each election, showing in red the states […]

Don’t ever change, social psychology! You’re perfect just the way you are

Richard Juster points us to this article, “Vocal characteristics predict infidelity intention and relationship commitment in men but not in women,” where by “men,” they meant 88 male college students, and by “women,” then meant 128 female college students, and by “predict,” they meant not very well. The story was featured in various classy news […]

“Everybody wants to be Jared Diamond”

As the saying goes, “Everybody wants to be Jared Diamond, that’s the problem.” (See also here and here.) The funny thing is, this principle also applies to . . . Jared Diamond himself! See this review by Anand Giridharadas, sent to me by Mark Palko.

“Stop me if you’ve heard this one before: Ivy League law professor writes a deepthoughts think piece explaining a seemingly irrational behavior that doesn’t actually exist.”

Under the heading, “Stop me if you’ve heard this one before,” Palko writes: An Ivy League law professor writes a deepthoughts think piece explaining a seemingly irrational behavior that doesn’t actually exist. (see here and here) My favorite bit is this, from the Ivy League law professor in question: What’s more, macroeconomists have typically spent […]

Misrepresenting data from a published source . . . it happens all the time!

Following up on yesterday’s post on an example of misrepresentation of data from a graph, I wanted to share a much more extreme example that I wrote about awhile ago, about some data misrepresentation in an old statistics textbook: About fifteen years ago, when preparing to teach an introductory statistics class, I recalled an enthusiastic […]

Alexey Guzey plays Stat Detective: How many observations are in each bar of this graph?

How many data points are in each bar of the top graph above? (See here for background.) It’s from this article: Milewski MD, Skaggs DL, Bishop GA, Pace JL, Ibrahim DA, Wren TA, Barzdukas A. Chronic lack of sleep is associated with increased sports injuries in adolescent athletes. Journal of Pediatric Orthopaedics. 2014 Mar 1;34(2):129-33. […]

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