ᴅʀ ᴍɪʀᴄᴇᴀ ᴢʟᴏᴛᴇᴀɴᴜ 🌺🌞🍃

🚨New paper: A Tutorial for Deception Detection Analysis or: How I Learned to Stop Aggregating Veracity Judgments and Embraced Signal Detection Theory Mixed Models w/ @vuorre A better way to analyse lie data. #deception #SDT #mixedeffects #brms #rstats link.springer.com/article/10.100…

#statstab #519 Sensitivity Analysis and Robustness Checks Thoughts: Multiverse analysis allows you to capture decision uncertainty. #multiverse #jackknife #bias #robustness #sensitivity #r #outliers …ta-analysis.share.connect.posit.cloud/sensitivity-an…

The preprint is available on Psyarxiv: osf.io/preprints/psya… We had not heard of people trying collaborative grant writing like this before. If you have experiences with this format you would like to share, let us know!

The mass replication studies published in Nature today are insane, exemplary and an enormous pile of work to improve science. It's just so awesome how many people spent time on this in return to be 1/100 coauthors on a thing. Just outstanding 🙌🙌🙌 1/

#statstab #518 Before p < 0.05 to Beyond p < 0.05: Using History to Contextualize p-Values and Significance Testing Thoughts: An appropriate paper for today. #pvalues #teaching #history #pedagogy #nhst #fisher tandfonline.com/doi/pdf/10.108…

#statstab #517 Generalizing Generalizability in Information Systems Research Thoughts: Do your findings generalise? What does that mean? #design #causalinference #theory #generalisability pubsonline.informs.org/doi/10.1287/is…

#statstab #516 Is there a boxplot variant for Poisson distributed data? Thoughts: People like their canned visualisations, but you need to think what question you are answering. #poisson #boxplot #sqrttransform #dataviz #r #outliers #distribution stats.stackexchange.com/questions/1308…

#statstab #515 Confounded dose-response effects of treatment adherence: fitting Bayesian instrumental variable models using brms Thoughts: A nice tutorial on how to think about confounding, Simulating data, and making inferences #brms #bayes #IV #guide rpsychologist.com/adherence-anal…

FAIR data and code is both the most boring part of Open Science, and the part that will most fundamentally change how science is done. This gap is regrettable, but understandable. Most scientists can't imagine what their science would be like if research outputs were FAIR.

#statstab #514 A puzzle of proportions Thoughts: "Two popular Bayesian tests can yield dramatically different conclusions" Model specification is important. #bayesian #bayes #bayesfactor #nulleffects #proportions doi.org/10.1002/sim.92…

#statstab #513 Some thoughts on checking the R session Thoughts: Maybe we stop using rm(list=ls())? Setting up a good environment isn't always intuitive. #rstats #r #coding #reproducibility #guide blog.djnavarro.net/posts/2026-01-…

#statstab #512 Standardised mean difference estimators {shinyapp} Thoughts: Calculating the correct SMD can be challenging, and most software are quite bad at it. Use this shiny app instead! #cohend #SMD #hedgesg #heterogeneity #effectsize effectsize.shinyapps.io/deffsize/

*powerful

#statstab #511 Seven Myths of Randomisation in Clinical Trials Thoughts: Randomization is a very power tool for inference. Closest we have to magic in research. But it's also misunderstood. #randomization #experiment #inference #design #bias #science methodologyhubs.mrc.ac.uk/files/9214/371…

#statstab #510 A Note on Dropping Experimental Subjects who Fail a Manipulation Check Thoughts: Another paper to consider when "removing participants who failed our manipulation check"🤷‍♂️ #manipulationcheck #estimand #experiment #bias #guide #missingdata doi.org/10.1017/pan.20…

#statstab #509 Effective sample size Thoughts: ESS often reported for bayesian models, but is it really understood? #brms #rstats #r #bayesian #ess #diagnostics statmodeling.stat.columbia.edu/2025/11/27/eff…

#statstab #508 Agreement Tests {SimplyAgree} Thoughts: "R package was created to make the process of quantifying measurement agreement, consistency, and reliability" #agreement #reliability #rstats #r #correlation #concordance aaroncaldwell.us/SimplyAgree/ar…

#statstab #507 Nonlinear models in {flocker} Thoughts: I'm thinking these are useful if my theory predicts some natural limit (L) for a process. Like memory recall. #nonlinear #glmm #brms #mem #asymtotic #flocker #r #rstats maths.bris.ac.uk/R/web/packages…

@RomanFolw Cool thread. Thanks for sharing!

@mzloteanu I mean, sure, most of the time VIF might not be a crucial for what you are up to (ie. you would rarely change your design or analysis based on "high" VIF values), but it still produces some information (stats.stackexchange.com/a/670842/152164).

#statstab #506 The VIF Score. What is it Good For? Absolutely Nothing Thoughts: Maybe we need to think more closely about what these metrics mean. #diagnostics #assumptions #regression #vif #variance #variableselection #error #multicolinearity journals.sagepub.com/doi/abs/10.117…

#statstab #505 Beyond Confounders Thoughts: What makes a good control and a bad control? #counterfactuals #confounder #DAG #r #modelling #selectionbias #variance #control #causalinference matheusfacure.github.io/python-causali…