iiinova — where your digital self keeps evolving

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moin syed @syeducation.bsky.social · 22/09/2026

New paper! Despite qualitative researchers claiming that generalizability is not a goal, their published work does indeed contain generalizable claims. We document this and provide recommendations for how all researchers can better calibrate their generalizability claims. doi.org/10.1177/2515...

article header for, "Qualitative Researchers Can and Do Generalize: Generalizability Claims in Qualitative Psychological Research". Metascience!

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Brendan Nyhan @brendannyhan.bsky.social · 10/09/2026

Extremely impressive field experiment by @nmalhotra.bsky.social et al. had few effects on election attitudes among conservatives in TX despite being "exposed to an average of 20 online videos, 18.5 cable TV videos, 23.9 display ads, 3 text messages, and 3 postcards" www.science.org/doi/full/10....

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Ben Tappin @benmtappin.bsky.social · 07/09/2026

Real sloppy Saloni, we demand better 😡😡😡

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Ben Tappin @benmtappin.bsky.social · 05/09/2026

+1. I sampled a handful of times and got like <3000 BCE for most of those and ended up living to an avg of something like 55. Was mega sus!

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Felix M. Simon @felixsimon.bsky.social · 02/09/2026

Back from my summer break with a piece for @transformernews.ai on why the impact of persuasive AI's will be smaller in the real world than some think because (1) it is hard to reach people and get their attention… buff.ly/TvWgFSH

transformernews.ai

AI is a worryingly-good persuader. But don’t panic, yet

AI systems are able to persuade people, but turning that ability into meaningful real-world influence may be harder

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LSE Department of Psychological & Behavioural Science @lsepbs.bsky.social · 01/09/2026

After six years as Head of the Department of Psychological and Behavioural Science, Professor Liam Delaney is handing over the role to Professor Alex Gillespie. We're grateful to Liam for his leadership and delighted to welcome Alex as our new Head of Department. Learn more 👇️

lse.ac.uk

Professor Alex Gillespie appointed Head of PBS | LSE

Professor Alex Gillespie has been appointed Head of LSE’s Department of Psychological and Behavioural Science, succeeding Professor Liam Delaney.

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Julia M. Rohrer @dingdingpeng.the100.ci · 27/08/2026

Psychological Science special issue call 📣 Ever had your research denigrated bc it's "only descriptive", "what's the mechanism though?" and "but what are the theoretical implications?" Now has come your time to shine. Send your best descriptive work! www.psychologicalscience.org/publications...

Psychological Science invites submissions that advance psychological science through careful description of psychological phenomena. Psychological research often focuses on explaining and predicting phenomena before we have a sufficiently detailed understanding of them. It can be difficult to build strong theories on incomplete empirical foundations of the phenomena themselves. Careful descriptive research helps establish shared empirical reference points developed around well-characterized phenomena. As such, descriptive research contributes to psychological science both by improving our understanding of the phenomena themselves and by providing a stronger foundation for future theory-building, explanation, and prediction.

We are interested in manuscripts that:

Deepen our understanding of psychological phenomena, including what they look like, how they vary, and how they unfold;
Draw on a variety of sources of evidence and methods, including qualitative, quantitative, and mixed-methods approaches; research conducted in naturalistic settings; intensive, longitudinal, or small-sample designs; and open or large-scale datasets;
Provide necessary context for interpreting what is being described, including who is represented in the data, the settings and circumstances in which phenomena occur, and the broader social contexts in which they are situated; and/or
Make a substantive contribution to psychological science through the quality, depth, or significance of the description provided.
The deadline for submitting regular manuscripts is April 15, 2027. Registered Reports will also be considered. The deadline for submitting Stage 1 Registered Reports is November 15, 2026. Authors should indicate in the “Comments to Editor” box during submission that the manuscript is intended for the “Descriptive Research in Psychological Science” special issue.

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Ben Tappin @benmtappin.bsky.social · 19/08/2026

It’s brilliant, one of my favourites in recent years

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vincentholst.bsky.social @vincentholst.bsky.social · 14/08/2026

As promised, we now share some details why it took 32 months to get this Matters Arising published, and, since many asked, share some comments on the reply to our critique. All details can be found in this small FAQ vincentholst.github.io/the_curious_.... Below is a summary 🧵

vincentholst.github.io

The Curious Case of the Declining Disruption’s Disappearance

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Jamie Cummins @jamiecummins.bsky.social · 20/07/2026

There are still a few days to apply to work with me and @malte.the100.ci as a PhD or postdoc on the development and evaluation of RegCheck. Help us research whether RegCheck works in practice in helping to reduce preregistration-paper discrepancies. Apply below!

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Julia M. Rohrer @dingdingpeng.the100.ci · 20/07/2026

New paper out now 🥳 When psychologists discuss generalisability, they often refer to vague notions of representativeness. We provide an accessible intro to the total survey error framework as a tool to reason about this more rigorously. w @taymalsalti.bsky.social @ruben.the100.ci >

Thinking Clearly About Sampling and Representation With the Total Survey Error Framework

Collecting a sample that represents the population of interest well constitutes a challenge across the social and behavioural sciences. Psychology in particular frequently relies on convenience samples—most notably students and, increasingly, online participants—with a tendency to either (implicitly) assume representativeness without substantive justification, or to acknowledge a lack of it only in passing. In contrast, researchers rarely engage with the actual implications for their inferences, which undermines the generalisability of psychological findings. Critically, representativeness must be defined with respect to variables relevant to the target of inference, rather than superficial demographic diversity. Here we present the Total Survey Error (TSE) framework as a methodological tool that systematically addresses the multifaceted sources of error—particularly those related to representation—that emerge throughout the research cycle. Although TSE originated in survey research, its principles are broadly applicable to any psychological study seeking inference from sample to population. We offer practical strategies for identifying, preventing, and mitigating representation errors to improve the credibility and generalisability of psychological research.

Illustration of the total survey error framework with the representation strand highlighted. It shows how coverage error, sampling error and non-response error arise during the sampling process.

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Ben Tappin @benmtappin.bsky.social · 20/07/2026

Awh ye. I haven’t dug in yet but this smells like a spiritual successor to Tal’s generalisability crisis AND it’s a “Thinking clearly about”. Double threat 🤙🤙🤙

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Brendan Nyhan @brendannyhan.bsky.social · 08/06/2026

Important @benmtappin.bsky.social on the need to carefully consider the relevant counterfactual when evaluating AI chatbots (also applies to social media!) benmtappin.substack.com/p/are-ai-cha...

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Georgia Tomova @georgiatomova.bsky.social · 13/05/2026

Why we should rethink causal mediation, and what to do instead? Come to hear the answer from Vanessa Didelez at the next CIIG seminar! The seminar will be hybrid. If you are in London, come join us in person at UCL! Otherwise, you can join on Zoom as usual. Registration links in comment below.

Vanessa Didelez, 8th June 2026 3 to 4.30pm. Why we should rethink causal mediation and what to do instead

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Michael Muthukrishna @michael.muthukrishna.com · 14/04/2026

How do you align AI in a world of plural, conflicting, and evolving human values? A starting point is human society itself. @sydneylevine.bsky.social and I are hiring a postdoc at NYU to combine insights from cultural evolution, computational moral cognition, and AI safety. Please share widely!1/

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Ben Tappin @benmtappin.bsky.social · 03/04/2026

It’s a little known fact that DAG stands for Dynamically Adjusted Gaslighting

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Ben Tappin @benmtappin.bsky.social · 27/03/2026

I wonder how these rates of sycophancy (and their effects) compare against realistic counterfactuals like talking with one’s close friends or spouse.

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Ben Ansell @benansell.bsky.social · 24/03/2026

📣 New job at Oxford's Centre for Advanced Social Science Methods (CASSM)! 📣 The Departments of Politics & IR (DPIR) and Social Policy and Intervention (DSPI) are hiring an Associate Professor of Causal and Experimental Methods. Come work with me and amazing Oxford peeps! Deadline NOON April 27th.

politics.ox.ac.uk

Associate Professorship of Causal and Experimental Methods in Politics and Social Policy

University salary from £58,265 - £77,645 per annum which is inclusive of an Oxford University Weighting of £1,730 p.aPermanent upon completion of a successful review. The review is conducted during th...

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Ben Tappin @benmtappin.bsky.social · 22/03/2026

Students skipping this lecture Not At Random

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Ben Tappin @benmtappin.bsky.social · 22/03/2026

The synth backing track takes the vibe to an unexpected place 🥲

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Ben Tappin @benmtappin.bsky.social · 21/03/2026

Closing out the teaching semester this week with one of my favourite topics. Sadly its pedagogy was forever and unforgivably mar'd by the truly worst naming convention of all time...

Terminator (missing data) stalking a scared child (researchers).

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Ben Tappin @benmtappin.bsky.social · 21/03/2026

I am impressed by your ability to successfully operationalize an integer scale for your feelings even though there is no true scale!

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Ben Tappin @benmtappin.bsky.social · 21/03/2026

Ahh sorry I understand! (Clearly my powers of understanding leave a lot to be desired.) I agree that if true this would be substantively interesting in addition to being predictively useful. Your suggestion reminds me of this paper, an instant classic for various reasons www.pnas.org/doi/full/10....

pnas.org

The scientific value of numerical measures of human feelings | PNAS

Human feelings measured in integers (my happiness is an 8 out of 10, my pain 2 out of 6) have no objective scientific basis. They are “made-up” num...

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Ben Tappin @benmtappin.bsky.social · 21/03/2026

I didn’t understand your comment at first—until I realised maybe you’re interpreting CV as curriculum vitae; I meant cross validation! 😅

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Ben Tappin @benmtappin.bsky.social · 21/03/2026

Yes. Then if you push on this the claim becomes “okay it may not cause but it’s still useful because it predicts”. But then it’s like if predictive accuracy was your goal the design and analysis should be different e.g., CV + more predictors. I’ve been fully Westfall & Yarkoni-pilled on this point.

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Lauren Leek @laurenleek.eu · 16/03/2026

Sydney named its socio-economic divide the "latte line" and has been arguing about who drew it for 20 years. London has the same divide and calls it "character." I built a machine learning model to do the impolite thing: draw it and blame someone. open.substack.com/pub/laurenle...

open.substack.com

London's Divide Was Called Character. It Was Actually Policy.

I built a machine learning model to find London's divide and you can enter your postcode to see which side you're on. We've been blaming the wrong people for it.

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Joey Politano🏳️‍🌈 @josephpolitano.bsky.social · 09/03/2026

someone at the pentagon frantically typing “Claude, open the strait of Hormuz for me, quickest possible strategy, make no mistakes.”

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Ben Tappin @benmtappin.bsky.social · 10/03/2026

Your Claude is getting more interesting chat than mine (sorry Ben’s Claude!). “Claude, we’re going to refactor <function name here>.R to speed it up. Please analyze bottlenecks and make a plan.” x147

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Ben Tappin @benmtappin.bsky.social · 10/03/2026

The character of Per’s posts increasingly remind me of when an LLM talks to itself (not derogatory)

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Julia M. Rohrer @dingdingpeng.the100.ci · 05/03/2026

The idea of the need for a control group may have been radical at the time, but trust me it's now generally accepted. This is not some fringe believe by methodological hardliners; it's established practice. www.clinicaltrialsabundance.blog/p/clinical-t...

clinicaltrialsabundance.blog

Clinical trial reforms that once seemed radical

How randomized controlled trials, preregistration, and results reporting became standard practice.

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Anne Scheel @annemscheel.bsky.social · 27/02/2026

New newspaper headline for your Intro to Causal Inference lecture just dropped

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Julia M. Rohrer @dingdingpeng.the100.ci · 26/02/2026

Planning my next conversation starter

“I don’t like tibbles”, “pipes are overrated”, “loops are great actually”, … what’s next?

Ah yes — the R contrarian character arc 😄
If we continue the escalation, the next stages usually look like:
• “Actually, base R plotting is more transparent.”
• “data.table was right all along.”
• “I write my own S3 methods for fun.”
• “Vectorization is just premature optimization avoidance.”
• “apply() is perfectly readable.”
• “Why depend on 40 packages for a left join?”
• “I don’t trust NSE.”
• “Magrittr changed evaluation semantics and nobody talks about it.”
• “One well-written for-loop beats clever code.”
• “I opened the tidyverse source code once and never recovered.”

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Ben Tappin @benmtappin.bsky.social · 27/02/2026

I too was very glad to see this! But I feel like the whole episode bodes badly for the future. It’s not sustainable to rely on the CEO of a private company to act against their financial self-interest in order to curtail high-risk AI deployment (here mass surveillance and fully autonomous weapons).

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Prolific @joinprolific.bsky.social · 26/02/2026

🔔 “How real is the LLM threat to online research in academia?” will be live today. Experts from Microsoft Research, MIT / Stanford, Max Planck Institute, and Prolific discuss the threat of agentic AI to online research, and how to protect against it. Link to join live below. #AcademicSky #Research

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Ben Tappin @benmtappin.bsky.social · 26/02/2026

Reposting for visibility. Many researchers still appear oblivious to this fact, which is terrifying! It should be included in every experiment design 101.

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Ryan Briggs @ryancbriggs.net · 11/02/2026

When I pitch academics on my paper on nulls one common and understandable reaction is "but they're probably noisy and thus uninformative nulls." This is true, but it misses the key realization that WE PUBLISH THE RESULT WHEN THE NOISY TEST IS P<0.05.

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Ryan Briggs @ryancbriggs.net · 11/02/2026

I have a new paper. We look at ~all stats articles in political science post-2010 & show that 94% have abstracts that claim to reject a null. Only 2% present only null results. This is hard to explain unless the research process has a filter that only lets rejections through.

It must be very hard to publish null results
Publication practices in the social sciences act as a filter that favors statistically significant results over null findings. While the problem of selection on significance (SoS) is well-known in theory, it has been difficult to measure its scope empirically, and it has been challenging to determine how selection varies across contexts. In this article, we use large language models to extract granular and validated data on about 100,000 articles published in over 150 political science journals from 2010 to 2024. We show that fewer than 2% of articles that rely on statistical methods report null-only findings in their abstracts, while over 90% of papers highlight significant results. To put these findings in perspective, we develop and calibrate a simple model of publication bias. Across a range of plausible assumptions, we find that statistically significant results are estimated to be one to two orders of magnitude more likely to enter the published record than null results. Leveraging metadata extracted from individual articles, we show that the pattern of strong SoS holds across subfields, journals, methods, and time periods. However, a few factors such as pre-registration and randomized experiments correlate with greater acceptance of null results. We conclude by discussing implications for the field and the potential of our new dataset for investigating other questions about political science.

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Felix M. Simon @felixsimon.bsky.social · 11/02/2026

@benmtappin.bsky.social I just was pointed to this which is much more thorough and arrives at the same conclusion: www.exponentialview.co/p/how-95-esc... The "95% fail" number is essentially meaningless

exponentialview.co

How “95%” escaped into the world – and why so many believed it

Challenging sloppy thinking

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Ben Tappin @benmtappin.bsky.social · 11/02/2026

Felix and friends looking closely at the details so you don’t have to 👌👇

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Felix M. Simon @felixsimon.bsky.social · 11/02/2026

A short note on questionable AI studies or why friends don’t let friends make %-claims based on small-n qualitative research interview reports New week, new AI newsletter from Marina and myself here at RISJ: buff.ly/ckaUSn9

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Ben Tappin @benmtappin.bsky.social · 10/02/2026

Excited to dig into this! Thanks for the work Luc and team. Quick question: what’s happening with the y axis labels in figure 2 (0-20-80-60 etc.)? At first I thought I was misunderstanding something about your measurement, but I can’t see where. Are they just typos or what?

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Ben Tappin @benmtappin.bsky.social · 07/02/2026

It’s that time of year again

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