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Category: Unexpectedly Difficult Statistical Concepts

[103] Mediation Analysis is Counterintuitively Invalid

Posted on September 26, 2022October 16, 2022 by Uri Simonsohn

Mediation analysis is very common in behavioral science despite suffering from many invalidating shortcomings. While most of the shortcomings are intuitive [1], this post focuses on a counterintuitive one. It is one of those quirky statistical things that can be fun to think about, so it would merit a blog post even if it were…

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[99] Hyping Fisher: The Most Cited 2019 QJE Paper Relied on an Outdated Stata Default to Conclude Regression p-values Are Inadequate

Posted on October 13, 2021October 27, 2021 by Uri Simonsohn

The paper titled "Channeling Fisher: Randomization Tests and the Statistical Insignificance of Seemingly Significant Experimental Results" (.htm) is currently the most cited 2019 article in the Quarterly Journal of Economics (372 Google cites). It delivers bad news to economists running experiments: their p-values are wrong. To get correct p-values, the article explains, they need to…

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[91] p-hacking fast and slow: Evaluating a forthcoming AER paper deeming some econ literatures less trustworthy

Posted on September 15, 2020August 16, 2021 by Uri Simonsohn

The authors of a forthcoming AER article (.pdf), "Methods Matter: P-Hacking and Publication Bias in Causal Analysis in Economics", painstakingly harvested thousands of test results from 25 economics journals to answer an interesting question: Are studies that use some research designs more trustworthy than others? In this post I will explain why I think their…

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[88] The Hot-Hand Artifact for Dummies & Behavioral Scientists

Posted on May 27, 2020November 18, 2020 by Uri Simonsohn

A friend recently asked for my take on the Miller and Sanjurjo's (2018; pdf) debunking of the hot hand fallacy. In that paper, the authors provide a brilliant and surprising observation missed by hundreds of people who had thought about the issue before, including the classic Gilovich, Vallone, & Tverksy (1985 .htm). In this post:…

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[80] Interaction Effects Need Interaction Controls

Posted on November 20, 2019February 11, 2020 by Uri Simonsohn

In a recent referee report I argued something I have argued in several reports before: if the effect of interest in a regression is an interaction, the control variables addressing possible confounds should be interactions as well. In this post I explain that argument using as a working example a 2011 QJE paper (.htm) that…

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[79] Experimentation Aversion: Reconciling the Evidence

Posted on November 7, 2019February 11, 2020 by Berkeley Dietvorst, Rob Mislavsky, and Uri Simonsohn

A PNAS paper (.htm) proposed that people object “to experiments that compare two unobjectionable policies” (their title). In our own work (.htm), we arrive at the opposite conclusion: people “don’t dislike a corporate experiment more than they dislike its worst condition” (our title). In a forthcoming PNAS letter, we identified a problem with the statistical…

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[71] The (Surprising?) Shape of the File Drawer

Posted on April 30, 2018January 23, 2019 by Leif Nelson

Let’s start with a question so familiar that you will have answered it before the sentence is even completed: How many studies will a researcher need to run before finding a significant (p<.05) result? (If she is studying a non-existent effect and if she is not p-hacking.) Depending on your sophistication, wariness about being asked…

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[70] How Many Studies Have Not Been Run? Why We Still Think the Average Effect Does Not Exist

Posted on March 9, 2018February 12, 2020 by Leif Nelson

We have argued that, for most effects, it is impossible to identify the average effect (datacolada.org/33). The argument is subtle (but not statistical), and given the number of well-informed people who seem to disagree, perhaps we are simply wrong. This is my effort to explain why we think identifying the average effect is so hard….

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[57] Interactions in Logit Regressions: Why Positive May Mean Negative

Posted on February 23, 2017July 28, 2022 by Uri Simonsohn

Of all economics papers published this century, the 10th most cited appeared in Economics Letters , a journal with an impact factor of 0.5.  It makes an inconvenient and counterintuitive point: the sign of the estimate (b̂) of an interaction in a logit/probit regression, need not correspond to the sign of its effect on the…

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[50] Teenagers in Bikinis: Interpreting Police-Shooting Data

Posted on July 14, 2016February 15, 2020 by Uri Simonsohn

The New York Times, on Monday, showcased (.htm) an NBER working paper (.pdf) that proposed that “blacks are 23.8 percent less likely to be shot at by police relative to whites.” (p.22) The paper involved a monumental data collection effort  to address an important societal question. The analyses are rigorous, clever and transparently reported. Nevertheless, I do…

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  • [103] Mediation Analysis is Counterintuitively Invalid

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Posts on similar topics

Unexpectedly Difficult Statistical Concepts
  • [103] Mediation Analysis is Counterintuitively Invalid
  • [99] Hyping Fisher: The Most Cited 2019 QJE Paper Relied on an Outdated Stata Default to Conclude Regression p-values Are Inadequate
  • [91] p-hacking fast and slow: Evaluating a forthcoming AER paper deeming some econ literatures less trustworthy
  • [88] The Hot-Hand Artifact for Dummies & Behavioral Scientists
  • [80] Interaction Effects Need Interaction Controls
  • [79] Experimentation Aversion: Reconciling the Evidence
  • [71] The (Surprising?) Shape of the File Drawer
  • [70] How Many Studies Have Not Been Run? Why We Still Think the Average Effect Does Not Exist
  • [57] Interactions in Logit Regressions: Why Positive May Mean Negative
  • [50] Teenagers in Bikinis: Interpreting Police-Shooting Data

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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..