Xeophon (@xeophon) on X

@AmpCode The best-looking gradient slop came from @OpenAIDevs gpt-5.2-codex xhigh. The invoice, OTOH, looks mediocre as well. Verdict: Codex > M2.1 > GLM > Claude > Gemini

Emily (@IamEmily2050) on X

I asked Grok 4 + Gemini 3 Pro + Opus 4.5 + GPT 5.2 pro one question (How do you describe the most beautiful human woman face in details?) Then took the answers to NotebookLM and asked to create a report. The Artist's Guide to Facial Aesthetics: A Manual on Structure, Harmony,

Statistically Efficient Ways to Quantify Added Predictive Value of New Measurements – Statistical Thinking

Researchers have used contorted, inefficient, and arbitrary analyses to demonstrated added value in biomarkers, genes, and new lab measurements. Traditional statistical measures have always been up to the task, and are more powerful and more flexible. It’s time to revisit them, and to add a few slight twists to make them more helpful.

A Pragmatic Approach to Statistical Testing and Estimation (PASTE)

The p-value has dominated research in education and related fields and a statistically non-significant p-value is quite commonly interpreted as ‘confirming’ the null hypothesis (H0) of ‘equivalence’. This is unfortunate, because p-values are not fit for that purpose. This paper discusses three alternatives to the traditional p-value that unfortunately have remained underused but can provide evidence in favor of ‘equivalence’ relative to ‘non-equivalence’: two one-sided tests (TOST) equivalence testing, Bayesian hypothesis testing, and information criteria. TOST equivalence testing and p-values both rely on concepts of statistical significance testing and can both be done with confidence intervals, but treat H0 and the alternative hypothesis (H1) differently. Bayesian hypothesis testing and the Bayesian credible interval aka posterior interval provide Bayesian alternatives to traditional p-values, TOST equivalence testing, and confidence intervals. However, under conditions outlined in this paper, confidence intervals and posterior intervals may yield very similar interval estimates. Moreover, Bayesian hypothesis testing and information criteria provide fairly easy to use alternatives to statistical significance testing when multiple competing models can be compared. Based on these considerations, this paper outlines a pragmatic approach to statistical testing and estimation (PASTE) for research in education and related fields. In a nutshell, PASTE states that all of the alternatives to p-values discussed in this paper are better than p-values, that confidence intervals and posterior intervals may both provide useful interval estimates, and that Bayesian hypothesis testing and information criteria should be used when the comparison of multiple models is concerned.

The limited epistemic value of ‘variation analysis’

While appeal to R squared is a common rhetorical device, it is a very tenuous connection to any plausible explanatory virtues for many reasons. Either it is meant to be merely a measure of predicta…

Integrated topographic corrections improve forest mapping using Landsat imagery

In mountainous environments, topography strongly affects the reflectance due to illumination effects and cast shadows, which introduce errors in land …

JASP - A Fresh Way to do Statistics

The Principle of Predictive Irrelevance, or Why Intervals Should Not be Used for Model Comparison Featuring a Point Null Hypothesis

This post summarizes Wagenmakers, E.-J., Lee, M. D., Rouder, J. N., & Morey, R. D. (2019). The principle of predictive irrelevance, or why intervals should not be used for model comparison feat…