Rutger Bregman on Twitter / X

Capitalism and Degrowth: An Impossibility Theorem - Monthly Review

A slightly different version of this article was published under the title “Degrow or Die?” in the December/January 2011 issue of the UK journal Red Pepper, for which it was... READ MORE

Andreas De Block on Twitter / X

It took Nature three years to publish this rebuttal, which clearly demonstrates the fundamental flaws in the original paper. In the meantime, the paper's conclusions influenced scientific debate and policy decisions. Such delays in correcting flawed research do real damage. https://t.co/QI3Ei3PE70— Andreas De Block (@DeblockBlock) August 13, 2026

Why Most Published Research Findings Are False

Summary There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.

Study Guide: One Day, Everyone Will Have Always Been Ag…

Analyzing literature can be hard — we make it easy! Thi…

Datamethods Discussion Forum

This is a place for discussions and Q&A about data-related issues and quantitative methods including study design, data analysis, and interpretation.

The natural selection of bad science

Abstract. Poor research design and data analysis encourage false-positive findings. Such poor methods persist despite perennial calls for improvement, sugg

“Statistical Significance” and Statistical Reporting: Moving Beyond Binary

Null hypothesis significance testing (NHST) is the default approach to statistical analysis and reporting in marketing and the biomedical and social sciences more broadly. Despite its default role, NHST has long been criticized by both statisticians and applied researchers, including those within marketing. Therefore, the authors propose a major transition in statistical analysis and reporting. Specifically, they propose moving beyond binary: abandoning NHST as the default approach to statistical analysis and reporting. To facilitate this, they briefly review some of the principal problems associated with NHST. They next discuss some principles that they believe should underlie statistical analysis and reporting. They then use these principles to motivate some guidelines for statistical analysis and reporting. They next provide some examples that illustrate statistical analysis and reporting that adheres to their principles and guidelines. They conclude with a brief discussion.

Elena Rossini on GoToSocial ⁂ (@elena@aseachange.com)

[3 media attachments] Dear friends, Please gather around for a long post about #WSocial and how they are responding to critiques. I have noticed a trend: W Social leaders and staunch supporters are describing any critical posts about the company as "targeted disinformation." Its CEO said they will soon have a technical solution in place for this. Oh?!? 🔗: https://mu.social/profile/anna.wsocial.eu/post/3mtqufy6g4s2r I suppose the "targeted disinformation" claims are about recent posts by the likes of me and @pallenberg about troubling hate speech that is allowed on W Social. Claims backed by hard evidence, in the form of screenshots and video captures. (I am increasingly doing screen recordings of problematic posts and interactions by W Social people because they have a tendency to delete evidence - like an infamous comment on a video by the Instutute for Remigration). ⬇️ ⬇️ ⬇️ Discrediting sources, renaming evidence as "disinformation" and creating a victim narrative has a long, storied history. ⬆️ ⬆️ ⬆️ But I'm digressing. I speak up about W Social because: - Mainstream media runs PR fluff about them; - Horrid hate speech on the platform is completely unmoderated. W Social is currently showing the limits of free speech absolutism. Its CEO wrote: "We don’t need W to become another echo chamber." Reminds you of something? Atmosphere user @echna.bsky.social astutely remarked that "W Social is on an X speedrun, without ever having been like Twitter." 🔗: https://mu.social/profile/echna.bsky.social/post/3mtqlknzork2t Bluesky, #Blacksky, and #Northsky have mechanisms in place to report hate speech - and I am super thankful for that. As of today, I cannot report to W Social posts by their users that contain hate speech. The W Social moderation list's only purpose is to make a distinction between verified users and bots. Compare it with Blacksky's moderation list and be amazed (see screenshots). What's the big deal about the lack of content moder…

(PDF) Post-truth and Disinformation: Using discourse analysis to understand the creation of emotional and rival narratives in Brexit

PDF | The present research explores the concept of post-truth and disinformation in regard to Brexit. It is a qualitative and exploratory investigation.... | Find, read and cite all the research you need on ResearchGate

I saw this meta-analysis shared a couple of times recently, so we took a look and re-analyzed the data. | František Bartoš

I saw this meta-analysis shared a couple of times recently, so we took a look and re-analyzed the data. We found that the conclusion is almost entirely driven by publication bias. Both state-of-the-art and standard methods reduce the degree of the effect 2-3 fold. Moreover, the data no longer show statistical evidence for the main conclusions. Importantly, our findings do not imply that there is no positive effect of ChatGPT, or other large language models on learning; in fact, our analysis reveals that there is “absence of evidence” rather than “evidence of absence”. The present literature appears to be contaminated by publication bias; high-quality registered reports are needed to properly evaluate the effect of large language models in educational settings. See the full response just submitted for publication at https://lnkd.in/eGKHHKtg

A Statistical Interrogation of “The Case for Causality, Part 1” by Rausch and Haidt – Matthew B. Jané

I don’t know anything about the literature on social media and mental health so my focus on this post is to interrogate the statistical approach taken by the article written by Zach Rausch and Jonathon Haidt (link here) and to some extent the original meta-analysis by Ferguson.

Sleuths flag ‘complete mismatch’ in data of BMJ stem cell study | Manoj Lalu

This is disappointing, but I’m not surprised. I was one of the reviewers for the initial version. You can see by my review (it’s all open) that I flagged primary outcome switching, a complete lack of any data on the cells themselves, and sample size inconsistencies in the protocol versus the report (and within the report). The data seemed impressive. Too good to be true I guess? I never saw the paper again in peer review after that initial review. The next notification I received was that the paper was accepted. Given BMJ’s commitment to open peer review and post-publication scrutiny (which I admire), I have no doubt we will hear about a formal editorial investigation soon.

Dylan Wiliam on Twitter / X

The best journal article titles tell you what the study actually found, rather than being a teaser to make you read the paper. Here's a good example: "Spaced mathematics practice improves test scores and reduces overconfidence" https://t.co/iXxdkAgYPM ($)— Dylan Wiliam (@dylanwiliam) December 31, 2024

Defusing the Depopulation Bomb

Book Review: "After the Spike" by Dean Spears and Michael Geruso