Category: Zombies

“Retire Statistical Significance”: The discussion.

So, the paper by Valentin Amrhein, Sander Greenland, and Blake McShane that we discussed a few weeks ago has just appeared online as a comment piece in Nature, along with a letter with hundreds (or is it thousands?) of supporting signatures. Following the first circulation of that article, the authors of that article and some […]

One more reason I hate letters of recommendation

Recently I reviewed a bunch of good reasons to remove letters of recommendation when evaluating candidates for jobs or scholarships. Today I was at a meeting and thought of one more issue. Letters of recommendation are not merely a noisy communication channel; they’re also a biased channel. The problem is that letter writers are strategic: […]

Statistical-significance filtering is a noise amplifier.

The above phrase just came up, and I think it’s important enough to deserve its own post. Well-meaning researchers do statistical-significance filtering all the time—it’s what they’re trained to do, it’s what they see in published papers in top journals, it’s what reviewers for journals want them to do—so I can understand why they do […]

Evidence distortion in clinical trials

After seeing our recent post, “Seeding trials”: medical marketing disguised as science, Till Bruckner sent me this message: I’ve been working on clinical trial transparency issues for over two years now, first for AllTrials and now for TranspariMED, and can assure you that this is only the tip of the iceberg. This report by Transparency […]

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 […]