Category: Zombies

Don’t worry, the post will be coming . . . eventually

Jordan Anaya sends along a link and writes: Not sure if you’re planning on covering this, but I noticed this today. This could also maybe be another example of the bullshit asymmetry principle since the original paper has an altmetric of 1300 and I’m not sure the rebuttal will get as much attention. I replied […]

“News Release from the JAMA Network”

A couple people pointed me to this: Here’s the Notice of Retraction: On May 8, 2018, notices of Expression of Concern were published regarding articles published in JAMA and the JAMA Network journals that included Brian Wansink, PhD, as author. At that time, Cornell University was contacted and was requested to conduct an independent evaluation […]

Statmodeling Retro

As many of you know, this blog auto-posts on twitter. That’s cool. But we also have 15 years of old posts with lots of interesting content and discussion! So I had this idea of setting up another twitter feed, Statmodeling Retro, that would start with our very first post in 2004 and then go forward, […]

More on that horrible statistical significance grid

Regarding this horrible Table 4: Eric Loken writes: The clear point or your post was that p-values (and even worse the significance versus non-significance) are a poor summary of data. The thought I’ve had lately, working with various groups of really smart and thoughtful researchers, is that Table 4 is also a model of their […]

The bullshit asymmetry principle

Jordan Anaya writes, “We talk about this concept a lot, I didn’t realize there was a name for it.” From the wikipedia entry: Publicly formulated the first time in January 2013 by Alberto Brandolini, an Italian programmer, the bullshit asymmetry principle (also known as Brandolini’s law) states that: The amount of energy needed to refute […]

When doing regression (or matching, or weighting, or whatever), don’t say “control for,” say “adjust for”

This comes up from time to time. We were discussing a published statistical blunder, an innumerate overconfident claim arising from blind faith that a crude regression analysis would control for various differences between groups. Martha made the following useful comment: Another factor that I [Martha] believe tends to promote the kind of thing we’re talking […]

Of butterflies and piranhas

John Cook writes: The butterfly effect is the semi-serious claim that a butterfly flapping its wings can cause a tornado half way around the world. It’s a poetic way of saying that some systems show sensitive dependence on initial conditions, that the slightest change now can make an enormous difference later . . . Once […]

How post-hoc power calculation is like a shit sandwich

Damn. This story makes me so frustrated I can’t even laugh. I can only cry. Here’s the background. A few months ago, Aleksi Reito (who sent me the adorable picture above) pointed me to a short article by Yanik Bababekov, Sahael Stapleton, Jessica Mueller, Zhi Fong, and David Chang in Annals of Surgery, “A Proposal […]

MRP (multilevel regression and poststratification; Mister P): Clearing up misunderstandings about

Someone pointed me to this thread where I noticed some issues I’d like to clear up: David Shor: “MRP itself is like, a 2009-era methodology.” Nope. The first paper on MRP was from 1997. And, even then, the component pieces were not new: we were just basically combining two existing ideas from survey sampling: regression […]

The post MRP (multilevel regression and poststratification; Mister P): Clearing up misunderstandings about appeared first on Statistical Modeling, Causal Inference, and Social Science.

MRP (multilevel regression and poststratification; Mister P): Clearing up misunderstandings about

Someone pointed me to this thread where I noticed some issues I’d like to clear up: David Shor: “MRP itself is like, a 2009-era methodology.” Nope. The first paper on MRP was from 1997. And, even then, the component pieces were not new: we were just basically combining two existing ideas from survey sampling: regression […]

The post MRP (multilevel regression and poststratification; Mister P): Clearing up misunderstandings about appeared first on Statistical Modeling, Causal Inference, and Social Science.

On deck for the first half of 2019

OK, this is what we’ve got for you: “The Book of Why” by Pearl and Mackenzie Reproducibility and Stan MRP (multilevel regression and poststratification; Mister P): Clearing up misunderstandings about Becker on Bohm on the important role of stories in science This is one offer I can refuse How post-hoc power calculation is like a […]

The post On deck for the first half of 2019 appeared first on Statistical Modeling, Causal Inference, and Social Science.

On deck for the first half of 2019

OK, this is what we’ve got for you: “The Book of Why” by Pearl and Mackenzie Reproducibility and Stan MRP (multilevel regression and poststratification; Mister P): Clearing up misunderstandings about Becker on Bohm on the important role of stories in science This is one offer I can refuse How post-hoc power calculation is like a […]

The post On deck for the first half of 2019 appeared first on Statistical Modeling, Causal Inference, and Social Science.

What to do when you read a paper and it’s full of errors and the author won’t share the data or be open about the analysis?

Someone writes: I would like to ask you for an advice regarding obtaining data for reanalysis purposes from an author who has multiple papers with statistical errors and doesn’t want to share the data. Recently, I reviewed a paper that included numbers that had some of the reported statistics that were mathematically impossible. As the […]

The post What to do when you read a paper and it’s full of errors and the author won’t share the data or be open about the analysis? appeared first on Statistical Modeling, Causal Inference, and Social Science.

What to do when you read a paper and it’s full of errors and the author won’t share the data or be open about the analysis?

Someone writes: I would like to ask you for an advice regarding obtaining data for reanalysis purposes from an author who has multiple papers with statistical errors and doesn’t want to share the data. Recently, I reviewed a paper that included numbers that had some of the reported statistics that were mathematically impossible. As the […]

The post What to do when you read a paper and it’s full of errors and the author won’t share the data or be open about the analysis? appeared first on Statistical Modeling, Causal Inference, and Social Science.

Authority figures in psychology spread more happy talk, still don’t get the point that much of the published, celebrated, and publicized work in their field is no good (Part 2)

Part 1 was here. And here’s Part 2. Jordan Anaya reports: Uli Schimmack posted this on facebook and twitter. I [Anaya] was annoyed to see that it mentions “a handful” of unreliable findings, and points the finger at fraud as the cause. But then I was shocked to see the 85% number for the Many […]

The post Authority figures in psychology spread more happy talk, still don’t get the point that much of the published, celebrated, and publicized work in their field is no good (Part 2) appeared first on Statistical Modeling, Causal Inference, and Social Science.

Authority figures in psychology spread more happy talk, still don’t get the point that much of the published, celebrated, and publicized work in their field is no good (Part 2)

Part 1 was here. And here’s Part 2. Jordan Anaya reports: Uli Schimmack posted this on facebook and twitter. I [Anaya] was annoyed to see that it mentions “a handful” of unreliable findings, and points the finger at fraud as the cause. But then I was shocked to see the 85% number for the Many […]

The post Authority figures in psychology spread more happy talk, still don’t get the point that much of the published, celebrated, and publicized work in their field is no good (Part 2) appeared first on Statistical Modeling, Causal Inference, and Social Science.

A couple of thoughts regarding the hot hand fallacy fallacy

For many years we all believed the hot hand was a fallacy. It turns out we were all wrong. Fine. Such reversals happen. Anyway, now that we know the score, we can reflect on some of the cognitive biases that led us to stick with the “hot hand fallacy” story for so long. Jason Collins […]

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