Rutger Bregman on Twitter / X

A brief summary of effective study methods — LessWrong

EDIT: Reworked and moved to Main following Gunnar_Zarncke's advice. …

On technology and degrowth (notes on Hickel 2023)

Jason Hickel is one of the leading voices in the degrowth/postgrowth movement, and recently laid out his thoughts on the relationship between degrowth and technology. He raises a number of importan…

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…

BishopBlog

Ramblings on academic-related matters. For information on my research see https://www.psy.ox.ac.uk/research/oxford-study-of-children-s-communication-impairments. Twin analysis blog: http://dbtemp.blogspot.com/ . ERP time-frequency analysis blog: bishoptechbits.blogspot.com/ . For tweets, follow @deevybee.

Data Colada

Thinking about evidence and vice versa

How the Cases You Choose Affect the Answers You Get: Selection Bias in Comparative Politics

This article demonstrates how the selection of cases for study on the basis of outcomes on the dependent variable biases conclusions. It first lays out the logic of explanation and shows how it is violated when only cases that have achieved the outcome of interest are studied. It then examines three well-known and highly regarded studies in the field of comparative politics, comparing the conclusions reached in the original work with a test of the arguments on cases selected without regard for their position on the dependent variable. In each instance, conclusions based on the uncorrelated sample differ from the original conclusions.

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.

Meta Research News | Meta Research

At Meta, research permeates everything we do. We believe the most interesting research questions are derived from real world problems.

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

The Cambridge Analytica Files | The Guardian

Latest The Cambridge Analytica Files news, comment and analysis from the Guardian, the world's leading liberal voice

“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.