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Jan Pfänder

@janpfa.bsky.social

150 followers 150 following 51 posts

phd student https://janpfander.github.io/

janpfander.github.io

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Jan Pfänder @janpfa.bsky.social · Sep 5

How much do people really reject science?

New paper out doi.org/10.1177/0963...

In four studies, we asked Americans—including flat Earthers, climate change deniers and vaccine skeptics—whether they accepted basic scientific facts.

The result? A surprisingly high level of agreement. 👇

Quasi-universal acceptance of basic science in the United States - Jan Pfänder, Lou Kerzreho, Hugo Mercier, 2025

Substantial minorities of the population report a low degree of trust in science, or endorse conspiracy theories that violate basic scientific knowledge. This m...

Reposted by Jan Pfänder

Jamie Cummins @jamiecummins.bsky.social · 23d

Can large language models stand in for human participants?
Many social scientists seem to think so, and are already using "silicon samples" in research.

One problem: depending on the analytic decisions made, you can basically get these samples to show any effect you want.

THREAD 🧵

The threat of analytic flexibility in using large language models to simulate human data: A call to attention

Social scientists are now using large language models to create "silicon samples" - synthetic datasets intended to stand in for human respondents, aimed at revolutionising human subjects research. How...

Jan Pfänder @janpfa.bsky.social · 27d

I'm sorry for your loss! The case of your dad is touching and provides hope that science rejection can be overcome. Surely you've been a great science explainer to him :)

Reposted by Jan Pfänder

Dietram A. Scheufele @dietram.bsky.social · Sep 3

"Acceptance of the scientific consensus was very high in the sample as a whole (95.1%), but also in every sub-sample (e.g. no trust in science: 87.3%) ... [P]eople are motivated to reject specific scientific beliefs, and not science as a whole."

journals.sagepub.com/doi/abs/10.1...

Sage Journals: Discover world-class research

Subscription and open access journals from Sage, the world's leading independent academic publisher.

journals.sagepub.com

Reposted by Jan Pfänder

Dan Vergano @danvergano.bsky.social · Sep 2

Quasi-universal acceptance of basic science in the United States journals.sagepub.com/doi/abs/10.1...

high in sample as a whole (95.1%) "also in every sub-sample (e.g. no trust in science: 87.3%; complete endorsement of flat Earth theory: 87.2%"

"motivated to reject specific scientific beliefs"

Quasi-universal acceptance of basic science in the United States - Jan Pfänder, Lou Kerzreho, Hugo Mercier, 2025

Substantial minorities of the population report a low degree of trust in science, or endorse conspiracy theories that violate basic scientific knowledge. This m...

journals.sagepub.com

Jan Pfänder @janpfa.bsky.social · Sep 5

A big thank you to my amazing co-authors Lou Kerzreho and @hugoreasoning.bsky.social

For access to a version of the paper, please check out my website janpfander.github.io/research/

Research – Jan Pfänder

janpfander.github.io

Jan Pfänder @janpfa.bsky.social · Sep 5

These results lead us to believe that, in many instances, science rejection might have nothing to do with the underlying science.

Instead, other factors (e.g. psychological traits or political ideology) are likely to be the key drivers of such rejections.

Jan Pfänder @janpfa.bsky.social · Sep 5

However, if people were genuinely distrusting science, as some claim to be, they should reject most or all of basic science knowledge. But they don’t.

Jan Pfänder @janpfa.bsky.social · Sep 5

Second, it suggests something about the psychology of science rejection.

One might think that the root of rejecting the scientific consensus on specific topics such as vaccines or climate change is genuine distrust of science.

Jan Pfänder @janpfa.bsky.social · Sep 5

Why does this quasi-universal acceptance of basic science matter?

First, it gives hope: science rejection does not appear to be wholesale.

Stressing basic science underlying controversial topics such as vaccines or climate change might help science communicators convincing skeptics.

Jan Pfänder @janpfa.bsky.social · Sep 5

On average, participants accepted the scientific consensus in 95% of cases.

Even participants who claimed they don’t trust science at all accepted the scientific consensus in 87% of cases.

Flat earthers accepted 87% of basic science claims.

Climate change deniers had an acceptance rate of 92%.

Jan Pfänder @janpfa.bsky.social · Sep 5

After each question, we showed participants the correct, scientifically consensual answer, with a short explanation and some links.

We then asked participants: Do you accept this answer?

Jan Pfänder @janpfa.bsky.social · Sep 5

We asked participants questions that are often used to test basic science knowledge, such as:

Are electrons smaller, larger, or the same size as atoms? [Smaller; Same size; Larger]

Jan Pfänder @janpfa.bsky.social · Sep 5

How much do people really reject science?

New paper out doi.org/10.1177/0963...

In four studies, we asked Americans—including flat Earthers, climate change deniers and vaccine skeptics—whether they accepted basic scientific facts.

The result? A surprisingly high level of agreement. 👇

Quasi-universal acceptance of basic science in the United States - Jan Pfänder, Lou Kerzreho, Hugo Mercier, 2025

Substantial minorities of the population report a low degree of trust in science, or endorse conspiracy theories that violate basic scientific knowledge. This m...

Reposted by Jan Pfänder

Julia M. Rohrer @dingdingpeng.the100.ci · Aug 25

Ever stared at a table of regression coefficients & wondered what you're doing with your life?

Very excited to share this gentle introduction to another way of making sense of statistical models (w @vincentab.bsky.social)
Preprint: doi.org/10.31234/osf...
Website: j-rohrer.github.io/marginal-psy...

Models as Prediction Machines: How to Convert Confusing Coefficients into Clear Quantities

Abstract
Psychological researchers usually make sense of regression models by interpreting coefficient estimates directly. This works well enough for simple linear models, but is more challenging for more complex models with, for example, categorical variables, interactions, non-linearities, and hierarchical structures. Here, we introduce an alternative approach to making sense of statistical models. The central idea is to abstract away from the mechanics of estimation, and to treat models as “counterfactual prediction machines,” which are subsequently queried to estimate quantities and conduct tests that matter substantively. This workflow is model-agnostic; it can be applied in a consistent fashion to draw causal or descriptive inference from a wide range of models. We illustrate how to implement this workflow with the marginaleffects package, which supports over 100 different classes of models in R and Python, and present two worked examples. These examples show how the workflow can be applied across designs (e.g., observational study, randomized experiment) to answer different research questions (e.g., associations, causal effects, effect heterogeneity) while facing various challenges (e.g., controlling for confounders in a flexible manner, modelling ordinal outcomes, and interpreting non-linear models).

Figure illustrating model predictions. On the X-axis the predictor, annual gross income in Euro. On the Y-axis the outcome, predicted life satisfaction. A solid line marks the curve of predictions on which individual data points are marked as model-implied outcomes at incomes of interest. Comparing two such predictions gives us a comparison. We can also fit a tangent to the line of predictions, which illustrates the slope at any given point of the curve.

A figure illustrating various ways to include age as a predictor in a model. On the x-axis age (predictor), on the y-axis the outcome (model-implied importance of friends, including confidence intervals).

Illustrated are
1. age as a categorical predictor, resultings in the predictions bouncing around a lot with wide confidence intervals
2. age as a linear predictor, which forces a straight line through the data points that has a very tight confidence band and
3. age splines, which lies somewhere in between as it smoothly follows the data but has more uncertainty than the straight line.

Reposted by Jan Pfänder

Nicolas Beauvais @nicolasbeauvais.bsky.social · Aug 21

Happy to share that my first paper is out in Thinking & Reasoning! 📄📢
With Aikaterini Voudouri, @boissinesther.bsky.social & @wimdeneys.bsky.social we show that deliberate reasoning helps not just to correct but also to justify intuitive judgments.

🔗Full paper: shorturl.at/JTeTi
Quick thread below!

Reposted by Jan Pfänder

Viktoria Cologna @colognaviktoria.bsky.social · Jul 1

🚨 How do exposure to extreme weather events and the subjective attribution of these events to climate change relate to climate policy support across the world? 🔥🌎

Find out more in our OA article published in Nature Climate Change 👇 1/7 🧵 www.nature.com/articles/s41...

Extreme weather event attribution predicts climate policy support across the world - Nature Climate Change

Literature produced inconsistent findings regarding the links between extreme weather events and climate policy support across regions, populations and events. This global study offers a holistic asse...

Reposted by Jan Pfänder

Daniel Lakens @lakens.bsky.social · Jun 17

Very excited to publicly share news about a new tool, Papercheck, that @debruine.bsky.social and me started to develop more than a year ago! In an introductory blog post, we explain our philosophy to automatically check scientific papers for best practices. daniellakens.blogspot.com/2025/06/intr...

Introducing Papercheck

Introducing Papercheck Introducing Papercheck An Automated Tool to Check for Best Practices in Scientifi...

daniellakens.blogspot.com

Reposted by Jan Pfänder

Nature Human Behaviour @nathumbehav.nature.com · Jun 16

How do laypeople make sense of scientific explanations? This new Article from @cruzf.bsky.social & @tanialombrozo.bsky.social looks at the role of jargon. They find that people find explanations with jargon satisfying, even though jargon reduces understanding.
www.nature.com/articles/s41...

How laypeople evaluate scientific explanations containing jargon - Nature Human Behaviour

Cruz and Lombrozo examine how laypeople make sense of scientific explanations and find that although jargon reduces understanding, for short explanations, jargon makes the explanation more satisfying.

Reposted by Jan Pfänder

Randall Munroe @xkcd.com · Jun 12

Good Science

xkcd.com/3101/

5-panel comic. (1) [teacher with long hair next to whiteboard] TEACHER: I’m supposed to give you the tools to do good science. (2) [teacher addressing students] But what *are* those tools? Methodology is hard and there are so many ways to get incorrect results. What is the magic ingredient that makes for good science? (3) TEACHER: To figure it out, I ran a regression with all the factors people say are important: [embedded list in sub-panel, cut off at end] Outcome variable: correct scientific results. Predictors: collaboration; skepticism of others’ claims; questioning your own beliefs; trying to falsify hypotheses; checking citations; statistical rigor; blinded analysis; financial disclosure; open data (4) TEACHER: The regression says two ingredients are the most crucial: 1) genuine curiosity about the answer to a question, and 2) ammonium hydroxide. (5) STUDENT: Wait, why did *ammonia* score so high? How did it even get on the list? LONG HAIR: ...And now you’re doing good science!

Reposted by Jan Pfänder

Jon Green @jongreen.bsky.social · Jun 12

tuxedo winnie the pooh meme

casual: caused by
formal: shaped by

Reposted by Jan Pfänder

Mike Wiser @drmikewiser.bsky.social · May 27

Ooh, I've run a session on basically "How to Conference" for the undergrad diversity program at the Evolution conference for the past few years. Some of what I cover:

Reposted by Jan Pfänder

Brendan Nyhan @brendannyhan.bsky.social · May 21

The country with the most sophisticated intelligence capabilities in the world, and the president is holding up
"a months-old blog post" from an obscure, conspiracy-mongering site "featuring a photo from the Democratic Republic of Congo" while making allegations against South Africa to its president

Bill McCarthy @billmccarthy.bsky.social · May 21

“Look, here's burial sites all over the place. These are all white farmers that are being buried,” Donald Trump said during today’s White House meeting with South Africa’s president.

The image he was holding up, however, is of the Democratic Republic of Congo.

www.barrons.com/news/trump-d...

Trump Displays DRC Visual As Proof Of South African 'Genocide'

US President Donald Trump brandished a stack of printed articles at the White House Wednesday that he claimed documented a genocide taking place against white people in South Africa.

www.barrons.com

Reposted by Jan Pfänder

Dean Eckles @eckles.bsky.social · May 18

If you randomize (or randomise) something, then everything in your study is causal, right?

Mark Rubin @markrubin.bsky.social · May 18

Misappropriating "Randomised"

"Randomisation is invoked here merely as a form of bureaucratic or regulatory insurance allowing a study to be labelled 'randomised'”.

@msaouel.bsky.social on the rise of "randomised non-comparative trials."

Bad stats: A regular series exploring slip-ups, snafus and salutary lessons from the world of statistics

Jan Pfänder @janpfa.bsky.social · May 15

Do you have workflows/other cloud services that circumvent this issue?

Or am I missing something and this is not really an issue?

Jan Pfänder @janpfa.bsky.social · May 15

But even if I publish the website via github, I generally want an additional, cloud-based backup of my local copy to

- safe files that are deliberately ignored for the public website
- have an easier restore process when changing/recovering a computer dies.