99% impossible: A valid, or falsifiable, internal meta-analysis.

Metaventory — the meta-science community

A comprehensive community of the people building meta-science and the tools they make — psychology and economics first, growing science-wide.

Meta must face youth addiction lawsuit by Massachusetts, court rules

The decision follows a landmark trial in which a ​jury found Meta and Google negligent for designing social media platforms that are harmful to young people.

Meta’s New AI Asked for My Raw Health Data—and Gave Me Terrible Advice

Meta’s Muse Spark model offers to analyze users’ health data, including lab results. Beyond the obvious privacy risks, it’s not a capable stand-in for a real doctor.

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

Bias, Skew, and Search Engines Are Sufficient to Explain Online Toxicity

Everyone Cheering The Social Media Addiction Verdicts Against Meta Should Understand What They’re Actually Cheering For

First things first: Meta is a terrible company that has spent years making terrible decisions and being terrible at explaining the challenges of social media trust & safety, all while prioritiz…

“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.

Opinion | Meta Is Dying. It’s About Time.

Meta has commenced a long, slow slide into irrelevance.

Opinion | Meta Is Dying. It’s About Time.

Meta has commenced a long, slow slide into irrelevance.

An Indian Trans Person’s Harrowing Encounter Fuels Backlash Against Meta AI ‘Pervert Glasses’

Karen Rebelo reports on incidents of abuse caused by use of Meta's wearable tech.

Research ethics: 3 ways to blow the whistle

Reporting suspicions of scientific fraud is rarely easy, but some paths are more effective than others.

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.