Category: Miscellaneous 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.

Combining apparently contradictory evidence

I want to write a more formal article about this, but in the meantime here’s a placeholder. The topic is the combination of apparently contradictory evidence. Let’s start with a simple example: you have some ratings on a 1-10 scale. These could be, for example, research proposals being rated by a funding committee, or, umm, […]

The post Combining apparently contradictory evidence appeared first on Statistical Modeling, Causal Inference, and Social Science.

Combining apparently contradictory evidence

I want to write a more formal article about this, but in the meantime here’s a placeholder. The topic is the combination of apparently contradictory evidence. Let’s start with a simple example: you have some ratings on a 1-10 scale. These could be, for example, research proposals being rated by a funding committee, or, umm, […]

The post Combining apparently contradictory evidence appeared first on Statistical Modeling, Causal Inference, and Social Science.

Combining apparently contradictory evidence

I want to write a more formal article about this, but in the meantime here’s a placeholder. The topic is the combination of apparently contradictory evidence. Let’s start with a simple example: you have some ratings on a 1-10 scale. These could be, for example, research proposals being rated by a funding committee, or, umm, […]

The post Combining apparently contradictory evidence appeared first on Statistical Modeling, Causal Inference, and Social Science.

“Thus, a loss aversion principle is rendered superfluous to an account of the phenomena it was introduced to explain.”

What better day than Christmas, that day of gift-giving, to discuss “loss aversion,” the purported asymmetry in utility, whereby losses are systematically more painful than gains are pleasant? Loss aversion is a core principle of the heuristics and biases paradigm of psychology and behavioral economics. But it’s been controversial for a long time. For example, […]

The post “Thus, a loss aversion principle is rendered superfluous to an account of the phenomena it was introduced to explain.” appeared first on Statistical Modeling, Causal Inference, and Social Science.

“Thus, a loss aversion principle is rendered superfluous to an account of the phenomena it was introduced to explain.”

What better day than Christmas, that day of gift-giving, to discuss “loss aversion,” the purported asymmetry in utility, whereby losses are systematically more painful than gains are pleasant? Loss aversion is a core principle of the heuristics and biases paradigm of psychology and behavioral economics. But it’s been controversial for a long time. For example, […]

The post “Thus, a loss aversion principle is rendered superfluous to an account of the phenomena it was introduced to explain.” appeared first on Statistical Modeling, Causal Inference, and Social Science.

The causal hype ratchet

Noah Haber informs us of a research article, “Causal language and strength of inference in academic and media articles shared in social media (CLAIMS): A systematic review,” that he wrote with Emily Smith, Ellen Moscoe, Kathryn Andrews, Robin Audy, Winnie Bell, Alana Brennan, Alexander Breskin, Jeremy Kane, Mahesh Karra, Elizabeth McClure, and Elizabeth Suarez, and […]

The post The causal hype ratchet appeared first on Statistical Modeling, Causal Inference, and Social Science.

The causal hype ratchet

Noah Haber informs us of a research article, “Causal language and strength of inference in academic and media articles shared in social media (CLAIMS): A systematic review,” that he wrote with Emily Smith, Ellen Moscoe, Kathryn Andrews, Robin Audy, Winnie Bell, Alana Brennan, Alexander Breskin, Jeremy Kane, Mahesh Karra, Elizabeth McClure, and Elizabeth Suarez, and […]

The post The causal hype ratchet appeared first on Statistical Modeling, Causal Inference, and Social Science.

Surprise-hacking: “the narrative of blindness and illusion sells, and therefore continues to be the central thesis of popular books written by psychologists and cognitive scientists”

Teppo Felin sends along this article with Mia Felin, Joachim Krueger, and Jan Koenderink on “surprise-hacking,” and writes: We essentially see surprise-hacking as the upstream, theoretical cousin of p-hacking. Though, surprise-hacking can’t be resolved with replication, more data or preregistration. We use perception and priming research to make these points (linking to Kahneman and priming, […]

The post Surprise-hacking: “the narrative of blindness and illusion sells, and therefore continues to be the central thesis of popular books written by psychologists and cognitive scientists” appeared first on Statistical Modeling, Causal Inference, and Social Science.

Surprise-hacking: “the narrative of blindness and illusion sells, and therefore continues to be the central thesis of popular books written by psychologists and cognitive scientists”

Teppo Felin sends along this article with Mia Felin, Joachim Krueger, and Jan Koenderink on “surprise-hacking,” and writes: We essentially see surprise-hacking as the upstream, theoretical cousin of p-hacking. Though, surprise-hacking can’t be resolved with replication, more data or preregistration. We use perception and priming research to make these points (linking to Kahneman and priming, […]

The post Surprise-hacking: “the narrative of blindness and illusion sells, and therefore continues to be the central thesis of popular books written by psychologists and cognitive scientists” 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 […]

The post A couple of thoughts regarding the hot hand fallacy fallacy 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 […]

The post A couple of thoughts regarding the hot hand fallacy fallacy appeared first on Statistical Modeling, Causal Inference, and Social Science.

Oh, I hate it when work is criticized (or, in this case, fails in attempted replications) and then the original researchers don’t even consider the possibility that maybe in their original work they were inadvertently just finding patterns in noise.

I have a sad story for you today. Jason Collins tells it: In The (Honest) Truth About Dishonesty, Dan Ariely describes an experiment to determine how much people cheat . . . The question then becomes how to reduce cheating. Ariely describes one idea: We took a group of 450 participants and split them into […]

The post Oh, I hate it when work is criticized (or, in this case, fails in attempted replications) and then the original researchers don’t even consider the possibility that maybe in their original work they were inadvertently just finding patterns in noise. appeared first on Statistical Modeling, Causal Inference, and Social Science.

Oh, I hate it when work is criticized (or, in this case, fails in attempted replications) and then the original researchers don’t even consider the possibility that maybe in their original work they were inadvertently just finding patterns in noise.

I have a sad story for you today. Jason Collins tells it: In The (Honest) Truth About Dishonesty, Dan Ariely describes an experiment to determine how much people cheat . . . The question then becomes how to reduce cheating. Ariely describes one idea: We took a group of 450 participants and split them into […]

The post Oh, I hate it when work is criticized (or, in this case, fails in attempted replications) and then the original researchers don’t even consider the possibility that maybe in their original work they were inadvertently just finding patterns in noise. appeared first on Statistical Modeling, Causal Inference, and Social Science.

My footnote about global warming

At the beginning of my article, How to think scientifically about scientists’ proposals for fixing science, which we discussed yesterday, I wrote: Science is in crisis. Any doubt about this status has surely been been dispelled by the loud assurances to the contrary by various authority figures who are deeply invested in the current system […]

The post My footnote about global warming appeared first on Statistical Modeling, Causal Inference, and Social Science.

My footnote about global warming

At the beginning of my article, How to think scientifically about scientists’ proposals for fixing science, which we discussed yesterday, I wrote: Science is in crisis. Any doubt about this status has surely been been dispelled by the loud assurances to the contrary by various authority figures who are deeply invested in the current system […]

The post My footnote about global warming appeared first on Statistical Modeling, Causal Inference, and Social Science.

Bayes, statistics, and reproducibility: “Many serious problems with statistics in practice arise from Bayesian inference that is not Bayesian enough, or frequentist evaluation that is not frequentist enough, in both cases using replication distributions that do not make scientific sense or do not reflect the actual procedures being performed on the data.”

This is an abstract I wrote for a talk I didn’t end up giving. (The conference conflicted with something else I had to do that week.) But I thought it might interest some of you, so here it is: Bayes, statistics, and reproducibility The two central ideas in the foundations of statistics—Bayesian inference and frequentist […]

The post Bayes, statistics, and reproducibility: “Many serious problems with statistics in practice arise from Bayesian inference that is not Bayesian enough, or frequentist evaluation that is not frequentist enough, in both cases using replication distributions that do not make scientific sense or do not reflect the actual procedures being performed on the data.” appeared first on Statistical Modeling, Causal Inference, and Social Science.

Bayes, statistics, and reproducibility: “Many serious problems with statistics in practice arise from Bayesian inference that is not Bayesian enough, or frequentist evaluation that is not frequentist enough, in both cases using replication distributions that do not make scientific sense or do not reflect the actual procedures being performed on the data.”

This is an abstract I wrote for a talk I didn’t end up giving. (The conference conflicted with something else I had to do that week.) But I thought it might interest some of you, so here it is: Bayes, statistics, and reproducibility The two central ideas in the foundations of statistics—Bayesian inference and frequentist […]

The post Bayes, statistics, and reproducibility: “Many serious problems with statistics in practice arise from Bayesian inference that is not Bayesian enough, or frequentist evaluation that is not frequentist enough, in both cases using replication distributions that do not make scientific sense or do not reflect the actual procedures being performed on the data.” appeared first on Statistical Modeling, Causal Inference, and Social Science.