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You are here		talyarkoni.org There is no ceiling effect in Johnson, Cheung, & Donnellan (2014) - [citation needed]
\|	\|	doomlab.github.io Dr. Erin Buchanan - Dr. Erin Buchanan	7.2 parsecs away Travel
\|	\|	Publications Statistical Packages & Tools Note: * indicate undergraduate authors, + graduate authors, $ both undergraduate and graduate Citation Link Buchanan, E. M. (2024). visualizemi: Visualization, Effect Size, and Replication of Measurement Invariance for Registered Reports. R package version 0.0.1. Link Buchanan, E. M. (2024). Visualizing Sensitivity. R package version 0.1.3. doi: 10.32614/CRAN.package.ViSe Link +Maxwell, N. P., & Buchanan, E. M. (2021). lrd: A Package for Processing Lexical Response Data.	7.2 parsecs away Travel
\|	\|	jaydaigle.net Hypothesis Testing and its Discontents, Part 2: The Conquest of Decision Theory	4.9 parsecs away Travel
\|	\|	This is the second-part of a three-part series on hypothesis testing. Today we'll look at the way we do hypothesis testing in practice, and how it tends to fail. Modern researchers use hypothesis testing as a tool to develop knowledge, but it's really a tool for making decisions, and so it encourages us to draw strong conclusions from weak evidence. It also encourages us to view studies that don't reject the null hypothesis as failures, which leads even honest and dedicated researchers to do shoddy resea...	4.9 parsecs away Travel
\|	\|	daniellakens.blogspot.com The 20% Statistician: Observed power, and what to do if your editor asks for post-hoc power analyses	5.7 parsecs away Travel
\|	\|	This blog post is now included in the paper "Sample size justification" available at PsyArXiv. Observed power (or post-hoc power) is th...	5.7 parsecs away Travel
\|	\|	yang-song.net Generative Modeling by Estimating Gradients of the Data Distribution \| Yang Song	27.9 parsecs away Travel
\|		This blog post focuses on a promising new direction for generative modeling. We can learn score functions (gradients of log probability density functions) on a large number of noise-perturbed data distributions, then generate samples with Langevin-type sampling. The resulting generative models, often called score-based generative models, has several important advantages over existing model families: GAN-level sample quality without adversarial training, flexible model architectures, exact log-likelihood ...	27.9 parsecs away Travel