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[33] "The" Effect Size Does Not Exist

Posted on February 9, 2015February 11, 2020 by Uri Simonsohn

Consider the robust phenomenon of anchoring, where people’s numerical estimates are biased towards arbitrary starting points. What does it mean to say “the” effect size of anchoring? It surely depends on moderators like domain of the estimate, expertise, and perceived informativeness of the anchor. Alright, how about “the average” effect-size of anchoring? That's simple enough….

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[32] Spotify Has Trouble With A Marketing Research Exam

Posted on January 12, 2015January 30, 2020 by Leif Nelson

This is really just a post-script to Colada [2], where I described a final exam question I gave in my MBA marketing research class. Students got a year’s worth of iTunes listening data for one person –me– and were asked: “What songs would this person put on his end-of-year Top 40?” I compared that list…

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[31] Women are taller than men: Misusing Occam’s Razor to lobotomize discussions of alternative explanations

Posted on December 18, 2014February 11, 2020 by Uri Simonsohn

Most scientific studies document a pattern for which the authors provide an explanation. The job of readers and reviewers is to examine whether that pattern is better explained by alternative explanations. When alternative explanations are offered, it is common for authors to acknowledge that although, yes, each study has potential confounds, no single alternative explanation…

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[30] Trim-and-Fill is Full of It (bias)

Posted on December 3, 2014February 11, 2020 by Uri, Joe, & Leif

Statistically significant findings are much more likely to be published than non-significant ones (no citation necessary). Because overestimated effects are more likely to be statistically significant than are underestimated effects, this means that most published effects are overestimates. Effects are smaller – often much smaller – than the published record suggests. For meta-analysts the gold…

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[29] Help! Someone Thinks I p-hacked

Posted on October 22, 2014April 22, 2020 by Uri Simonsohn

It has become more common to publicly speculate, upon noticing a paper with unusual analyses, that a reported finding was obtained via p-hacking. This post discusses how authors can persuasively respond to such speculations. Examples of public speculation of p-hacking Example 1. A Slate.com post by Andrew Gelman suspected p-hacking in a paper that collected…

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[28] Confidence Intervals Don't Change How We Think about Data

Posted on October 8, 2014February 11, 2020 by Uri Simonsohn

Some journals are thinking of discouraging authors from reporting p-values and encouraging or even requiring them to report confidence intervals instead. Would our inferences be better, or even just different, if we reported confidence intervals instead of p-values? One possibility is that researchers become less obsessed with the arbitrary significant/not-significant dichotomy. We start paying more…

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[27] Thirty-somethings are Shrinking and Other U-Shaped Challenges

Posted on September 17, 2014February 11, 2020 by Leif and Uri

A recent Psych Science (.pdf) paper found that sports teams can perform worse when they have too much talent. For example, in Study 3 they found that NBA teams with a higher percentage of talented players win more games, but that teams with the highest levels of talented players win fewer games. The hypothesis is easy enough…

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[26] What If Games Were Shorter?

Posted on August 22, 2014February 11, 2020 by Joe Simmons

The smaller your sample, the less likely your evidence is to reveal the truth. You might already know this, but most people don’t (.html), or at least they don’t appropriately apply it (.html). (See, for example, nearly every inference ever made by anyone). My experience trying to teach this concept suggests that it’s best understood…

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[25] Maybe people actually enjoy being alone with their thoughts

Posted on July 22, 2014March 20, 2016 by Leif Nelson

Recently Science published a paper concluding that people do not like sitting quietly by themselves (.html). The article received press coverage, that press coverage received blog coverage, which received twitter coverage, which received meaningful head-nodding coverage around my department. The bulk of that coverage (e.g., 1, 2, and 3) focused on the tenth study in…

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[24] P-curve vs. Excessive Significance Test

Posted on June 27, 2014February 12, 2020 by Uri Simonsohn

In this post I use data from the Many-Labs replication project to contrast the (pointless) inferences one arrives at using the Excessive Significant Test, with the (critically important) inferences one arrives at with p-curve. The many-labs project is a collaboration of 36 labs around the world, each running a replication of 13 published effects in…

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    © 2021, Uri Simonsohn, Leif Nelson, and Joseph Simmons. For permission to reprint individual blog posts on DataColada please contact us via email..