#statstab #384 When to use Fisher versus Neyman-Pearson framework? Thoughts: 13y old post, still a good read today. Uni season is almost upon us, so it's good to learn this stuff. #NHST #pvalues #RAFisher #NeymanPearson #Fisher #forum #statsexchange stats.stackexchange.com/questions/2314…

Today Dr April Smith & I talked implicit bias — and how to actually teach criminology students to identify it. 🎙️ Podcast drops soon… Research paper is here: doi.org/10.1080/105112… #Criminology #ImplicitBias Dr April Smith

#statstab #385 Understanding and Implementing Robust Regression in R Thoughts: Not a thorough guide, but shows how simple it is to use a robust regression (rlm vs lm). #R #regression #robust #statistics #guide #tutorial #outliers #Mestimator spsanderson.com/steveondata/po…

#statstab #386 {bayestestR} Evaluating Evidence and Making Decisions using Bayesian Statistics by Mattan S. Ben-Shachar 🎗️🇮🇱🇺🇦 Thoughts: Want to start using Bayesian stats? Here is a quick but comprehensive guide in #R #bayesian #bayes #mcmc #easystats #guide mattansb.github.io/bayesian-evide…

#statstab #387 Give Your Hypotheses Space! Thoughts: "It’s tempting to throw a bunch of variables...into a model ...but proceed at your own caution!" #Mbias #causalinference #collider #moderator #confounder #regression #r #DAG brian-lookabaugh.github.io/website-brianl…

#statstab #388 The odds are it's wrong: Correcting a common mistake in statistics Thoughts: Report probabilities instead, which in R (not SPSS) can be easily computed for your models. #odds #oddsratios #riskratios #probability #r #logisticregression onlinelibrary.wiley.com/doi/10.1111/te…

#statstab #389 Risk Ratio, odds ratio, risk difference… Which causal measure is easier to generalize? Thoughts: Use this with #388 for better insight into the issue of risk, odds, probabilities, and more. #odds #oddsratios #riskratios #probability arxiv.org/html/2303.1600…

#statstab #390 modelbased: An R package to make the most out of your statistical models through marginal means, marginal effects, and model predictions Thoughts: Great package for getting predicted probabilities for your models. #rstats #r #easystats doi.org/10.21105/joss.…

We spend so much time working working working… when is the last time you just relaxed? I can’t even remember, so I promised myself this weekend I would pick some fruit, read a book, and cook something new #AcademicTwitter #WorkLifeBalance

#statstab #391 {sensemakr} Sensitivity Analysis Tools for OLS Thoughts: No unobserved variables is an untestable assumption, but you can quantify the robustness of your ATE. #R #causalinference #observational #inference #confounding #bias #sensitivity carloscinelli.com/sensemakr/

#statstab #392 Statistically Efficient Ways to Quantify Added Predictive Value of New Measurements Thoughts: Forums can be great for asking the author for exact answers to complex questions #modelselection #causalinference #prediction #bias #information discourse.datamethods.org/t/statisticall…

#statstab #393 Statistically Efficient Ways to Quantify Added Predictive Value of New Measurements [actual post] Thoughts: #392 has the comments, but this is where the magic happens. #modelselection #modelcomparison #variance #effectsize #tutorial fharrell.com/post/addvalue/

#statstab #394 Difference-in-Differences Estimation Thoughts: A bit of love for the python coders. DiD with lots of examples and estimators. #did #python #guide #observational #TWFE #causalinference py-econometrics.github.io/pyfixest/diffe…

#statstab #395 The n-of-1 randomized controlled trial: clinical usefulness Thoughts: Could RCTs comprised of 1 participant be useful? Maybe! And more so than large sample observational studies. #Nof1 #RCTs #causalinference #research #methods #clinical pubmed.ncbi.nlm.nih.gov/2297206/

#statstab #396 If researchers find Cohen’s d = 8, no they didn’t Thoughts: Sometimes an effect is so impressive that its unbelievable. #effectsize #cohend #QRPs #sesoi mmmdata.io/posts/2025/07/…

#statstab #397 How and Why I Switched from the ROC Curve to the Precision-Recall Curve to Analyze My Imbalanced Models Thoughts: Can the eyewitness peeps explain how they correct for stimuli imbalance? 🤔 #roc #roccurve #precisionaccuracy #auc juandelacalle.medium.com/how-and-why-i-…

#statstab #398 Eta^2 for bayesian models {effectsize} Thoughts: Great resource, but scroll to "Eta Squared from Posterior Predictive Distribution" #effectsize #eta2 #bayesian #brms #r easystats.github.io/effectsize/ref…

#statstab #399 An Illustrated Guide to TMLE, Part II: The Algorithm Thoughts: Targeted Maximum Likelihood Estimation. Not sure what this is or if I'll ever use it, but the tutorial seems straightforward. #guide #tutorial #TMLE #binary #ML khstats.com/blog/tmle/tuto…