Jan Pfänder

Jan Pfänder

@janpfa.bsky.social

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

218 followers194 following71 posts

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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. 👇

doi.orgQuasi-universal acceptance of basic science in the United States - Jan Pfänder, Lou Kerzreho, Hugo Mercier, 2025Substantial minorities of the population report a low degree of trust in science, or endorse conspiracy theories that violate basic scientific knowledge. This m...

What we find: The more precise and consensual participants perceived a discipline's findings to be, the more they trusted its scientists. This held across disciplines, within disciplines, and for science in general.

The US pattern replicates in France: participants trusted biologists (M = 3.78) and physicists (M = 3.68) more than climate scientists (M = 3.47), sociologists (M = 3.14), and economists (M = 2.94) on a 1-5 scale.

Why these gaps? Existing accounts point to ideology: people trust those scientists they see as ideologically like-minded more. We tested a complementary explanation: people trust scientists whose findings they perceive as precise and consensual more.

Research on trust in science shows big gaps between disciplines, but almost all of it comes from the US. We asked a representative sample of the French population (N = 1,012) about their trust in five disciplines.

Jan Pfänder reposted

Why do societies rely on rigid moral rules? Don't lie. Keep your promises. Don't steal. We all recognize there are good reasons to make exceptions. So why are moral rules so often categorical? Our new paper proposes an answer. 🧵

Jan Pfänder reposted

1/ "Silicon samples" are becoming more and more common in research and polling. One problem: depending on the analytic decisions made, you can basically get these samples to show any effect you want. The updated version of this preprint is now online! THREAD🧵 arxiv.org/abs/2509.13397

arxiv.orgThe threat of analytic flexibility in using large language models to simulate human dataSocial scientists are now using large language models to create "silicon samples": synthetic datasets intended to stand in for human respondents. However, producing these samples requires many analyti...
Jamie Cummins@jamiecummins.bsky.social

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 🧵

Jan Pfänder reposted

Jan Pfänder reposted

Taking a look at Castro-Schilo & Grimm about "residualized change vs diff scores" -- interesting case of how spelling out estimands affects reasoning. Estimand of interest: "effect of prior cohabitation on change in relationship satisfaction pre/post marriage"> journals.sagepub.com/doi/10.1177/...

Nicholas Judd@njudd.com

Nice Post! Odd citation in the ANCOVA footnote for the change in observational analyses. I feel like it warrants a sentence on how this gives easy, large false positives: Van Breukelen, 2006; Sorjonen et al., 2019 & my absolute favorite, must read, Castro-Schilo, & Grimm, 2018

Jan Pfänder reposted

This recent RCT of an "AI stethoscope" claims the technology "shows promise" for diagnosing cardiovascular conditions. It does not. It is a textbook example of the risks of conducting unprincipled 'per protocol analyses'. Once again, peer review at a major medical journal has failed. 🧵 1/

The Lancet@thelancet.com

AI-enabled stethoscopes show promise for improving diagnosis of cardiovascular conditions, UK trial finds. Explore the research: spkl.io/63326AsjJ8

Jan Pfänder reposted

🔴 Gallicagram v2, nous voici ! 🔴 Nouvelle interface en react vachement plus stable et rapide, nouveaux corpus (Mediapart, Libé, le Parisien, Le Figaro…), recherche contextuelle, comparaisons inter-corpus, filtre rubrique, bilingue, infinite scrolling... on vous explique tout ! 📌 www.gallicagram.com

Jan Pfänder reposted

What motivates people to engage in climate advocacy? In a new PNAS Nexus megastudy [https://doi.org/10.1093/pnasnexus/pgaf400] led by @dgoldwert.bsky.social we tested 17 theoretical interventions on a large US sample (N=31,324) to increase public, political, and financial climate advocacy. 1/5

Jan Pfänder reposted

We've got ISSUES. Literally. We scraped >100k special issues & over 1 million articles to bring you a PISS-poor paper. We quantify just how many excess papers are published by guest editors abusing special issues to boost their CVs. How bad is it & what can we do? arxiv.org/abs/2601.07563 A 🧵 1/n

Jan Pfänder reposted

Too many significance tests!! Made this little graphic for my #stats class, showing the various kinds of (N)HST and how interpreting confidence intervals can replace all of them. Made with #rstats #ggplot (duh)

Jan Pfänder reposted

Jan Pfänder reposted

1. Transparency is necessary for credibility 2. Transparency is hard to change 3. Require transparency* 4. Transparency is not magic 5. Journals are part of problem 6. Expect more from journals 7. Peer review is not magic 8. A crisis can look a lot like „normal“ science 9. Meta-analysis is not magic

Peer Community In@peercommunityin.bsky.social

In case you have missed Simine Vazire's excellent webinar yesterday, here is the link to watch it online: youtu.be/_vb1CNwC3CM Thanks again @simine.com for staying up so late and thanks to the audience for the great questions!

Jan Pfänder reposted

Scientific Reports has a ⬆️ Impact Inflation: a very high IF given their citation network (self-citing, citation cartels, etc). They'll even typeset & publish AI slop for a fee! Strain: bit.ly/StrainQSS Strain explorer β: pagoba.shinyapps.io/strain_explo... #SciPub #ResearchIntegrity #AcademicSky

Erik Angner@erikangner.com

"Runctitiononal features"? "Medical fymblal"? "1 Tol Line storee"? This gets worse the longer you look at it. But it's got to be good, because it was published in Nature Scientific Reports last week: www.nature.com/articles/s41... h/t @asa.tsbalans.se

Jan Pfänder reposted

There is no reason why systematic reviews can't be open. The data used for synthesis is *already* open and there are many excellent open source tools that can facilitate the easy sharing of analysis scripts. Here's a nice guide for performing open systematic reviews doi.org/10.1525/coll...

Jan Pfänder reposted

new paper by Sean Westwood: With current technology, it is impossible to tell whether survey respondents are real or bots. Among other things, makes it easy for bad actors to manipulate outcomes. No good news here for the future of online-based survey research

Jan Pfänder reposted

🎉 New preprint: Bayesian Competence Inference guides Knowledge Attribution and Information search If someone knows that Venus is the only planet in the Solar System that rotates clockwise, will they also know what Earth’s only natural satellite is? What about which planets have no moons at all?

🎉 You’ve exceeded even our most optimistic expectations — we received 107 intervention proposals. THANK YOU! 🕵 Our advisory board will now begin reviewing all interventions. 🔗 More information: janpfander.github.io/trust_climat...

Viktoria Cologna@colognaviktoria.bsky.social

Climate science is facing significant opposition in the US. Today we are launching the collaborative Strengthening Trust in Climate Scientists Megastudy 📈 Find out more and join our efforts 👇🧵

Jan Pfänder reposted

"For instance, randomized controlled trials could explicitly manipulate multilingualism"

Casper Albers 🟥@casperalbers.nl

Doing non-causal inference (and being explicit about it), yet using a causal word as second word in the title. If you pay Nature € 10.690, they will publish this in Nature Ageing. I can tell you what I think of that for free. www.nature.com/articles/s43...

Jan Pfänder reposted

LLMs are now widely used in social science as stand-ins for humans—assuming they can produce realistic, human-like text But... can they? We don’t actually know. In our new study, we develop a Computational Turing Test. And our findings are striking: LLMs may be far less human-like than we think.🧵

arxiv.orgComputational Turing Test Reveals Systematic Differences Between Human and AI LanguageLarge language models (LLMs) are increasingly used in the social sciences to simulate human behavior, based on the assumption that they can generate realistic, human-like text. Yet this assumption rem...