The post critiques the paper “Uncertainty limits the use of power analysis”. It highlights issues with power analysis because of uncertainty deriving from sampling variability and fluctuating population effect sizes. While acknowledging valid points, I believe the paper’s conclusions are overly dismissive and argue for a refined approach to power and sample size estimation, if one accepts that uncertainty is a problem.

In the spirit of “if it took you a while to find out how to do something, write about it”, I will detail a method to approximate the surface area of a 3D shape. Our application here was finding the surface area of a cell but it can be used on any shape. We start with a 3D point set, specified by points of interest in a single cell imaged by 3D confocal microscopy.

I’ve been finishing up a project that uses ordered Beta regression (Kubinec 2022), a neat combination of Beta regression and ordered logistic regression that you can use for modeling continuous outcomes that are bounded on either side (in my project, we’re modeling a variable that can only be between 1 and 32, for instance). It’s possible to use something like zero-one-inflated Beta regression for outcomes like this, but that kind of model

In a project I’m working on, I need to generate predictions from a logistic regression model.

I recently posted a guide (mostly for future-me) about how to analyze conjoint survey data with R. I explore two different estimands that social scientists are interested in—causal average marginal component effects (AMCEs) and descriptive marginal means—and show how to find them with R, with both frequentist and Bayesian approaches. However, that post is a little wrong. It’s not wrong wrong, but it is a bit oversimplified.

Working on translating a key to the European shield bug nymphs (Puchkov, 1961) I thought I would look for pictures of the earlier life stages (nymphs, Fig. 1) of shield bugs (Pentatomoidea) on iNaturalist and found few observations actually had the life stage annotation.

In my research, I study international nongovernmental organizations (INGOs) and look at how lots of different institutional and organizational factors influence INGO behavior. For instance, many authoritarian regimes have passed anti-NGO laws and engaged in other forms of legal crackdown, which has forced NGOs to change their programming strategies and their sources of funding.

rOpenSci is very excited to announce our first peer-reviewed statistical Rpackages! One of rOpenSci’s core programs is software peer-review, where we use bestpractices from software engineering and academic peer-review to improvescientific software. Through this, we aim to make scientific software morerobust, usable, and trustworthy, and build a supportive community of practitioners.

As a field, statistics is really bad at naming things. Take, for instance, the term “fixed effects.” In econometrics and other social science-flavored statistics, this typically refers to categorical terms in a regression model.

Diagrams! You can download PDF, SVG, and PNG versions of the marginal effects diagrams in this guide, as well as the original Adobe Illustrator file, here: PDFs, SVGs, and PNGs Illustrator .ai file Do whatever you want with them! They’re licensed under Creative Commons Attribution-ShareAlike (BY-SA 4.0). I’m a huge fan of doing research and analysis in public.