#statstab #450 Kendall's Tau {video}

Thoughts: understanding nonparametric correlations can be difficult. This video is quite ok at explaining it.

#kendall #tau #correlation #nonoarametric #ranking #rank

https://numiqo.com/tutorial/kendalls-tau

#statstab #479 Uncertainty limits the use of power analysis

Thoughts: Frequentists avoiding uncertainty is never good.

#poweranalysis #error #uncertainty #simulation #cohend #effectsize

https://www.researchgate.net/publication/361158443_Uncertainty_limits_the_use_of_power_analysis

#statstab #478 Stuck between Zero and One: Modelling Non-Count Proportions with Beta and Dirichlet Regression

Thoughts: A few resources for proportion data.

#Dirichlet #proportions #betareg #count #tutorial

https://methodsblog.com/2019/08/06/beta-dirichlet-regression_en/

#statstab #477 Simulating data for Dirichlet regression with varying estimates

Thoughts: Interesting thread about an underused model.

#Dirichlet #brms #poweranalysis #rstats #proportions #stan #forum

https://discourse.mc-stan.org/t/simulating-data-for-dirichlet-regression-with-varying-estimates/35325

#statstab #476 In linear regression, when is it appropriate to use the log of an independent variable instead of the actual values?

Thoughts: Great thread explaining the (few) instances when log transforming makes sense

#logarithm #transformation #data

https://stats.stackexchange.com/questions/298/in-linear-regression-when-is-it-appropriate-to-use-the-log-of-an-independent-va/3530#3530

#statstab #475 {tinyplot} A lightweight extension of the base R graphics

Thoughts: Cool package for easy plotting of simple data.

#dataviz #ggplot #rstats #r #plots #figures #graphs

https://grantmcdermott.com/tinyplot/index.html

#statstab #474 Linear Models with Heterogeneous Coefficients

Thoughts: Sometimes you need more complicated models even if identification gets messy.

#heterogeneity #modelling #nonlinear #economics #econometrics

https://vladislav-morozov.github.io/econometrics-heterogeneity/linear/linear-introduction.html

#statstab #473 Synthesis without meta-analysis (SWiM) in systematic reviews

Thoughts: An interesting proposition for when you don't have good data for a meta-analysis.

#synthesis #metaanalysis #systematicreview

https://www.bmj.com/content/368/bmj.l6890

#statstab #472 Practical Bayesian Inference in Neuroscience: Or How I Learned To Stop Worrying
and Embrace the Distribution

Thoughts: Good intro to bayesian inference.

#bayesian #likelihood #tutorial

https://www.biorxiv.org/content/10.1101/2023.11.19.567743v2.full.pdf

#statstab #471 Give Your Hypotheses Space!

Thoughts: "each hypothesis requires its own model" + "Only interpret the output for your exposure of interest"

#causalinference #modelling #hypothesis #tutorial #confounding #mbias

https://brian-lookabaugh.github.io/website-brianlookabaugh/blog/2025/mutual-adjustment/

#statstab #470 {nmfspalette} palette according to NOAA Fisheries branding

Thoughts: Idk how I came across this package, but it has some ok colour combos.

#ggplot #dataviz #color #colour #themes #fish

https://nmfs-ost.github.io/nmfspalette/

#statstab #469 Significance tests, p-values, and falsificationism

Thoughts: An "interesting" thread of philosophy of science and debating.

#discussion #debate #pvalue #falsification #nhst

https://discourse.datamethods.org/t/significance-tests-p-values-and-falsificationism/

#statstab #468 Why Summaries of Research on Psychological Theories are Often Uninterpretable

Thoughts: psychology uses significance testing backwards relative to hard sciences.

#meehl #theory #criticism #falsification #popper #methods #nhst #psychology

https://scienceplusplus.org/metascience/assets/Meehl1990_1985.pdf

#statstab #467 Hypothesis testing, model selection, model comparison some thoughts

Thoughts: An excellent (but too short) discussion on bayesian inference.

#bayesian #bayesfactor #modelselection #inference #NBHT #BF #ROPE #primer

https://discourse.mc-stan.org/t/hypothesis-testing-model-selection-model-comparison-some-thoughts/19163

#statstab #466 Bayesian workflow: Prior determination, predictive checks and sensitivity analyses

Thoughts: Having a good bayesian work flow can be challenging with complex models.

#priors #bayesian #sensitivityanalysis #posterior #ppc #brms

https://pablobernabeu.github.io/2022/bayesian-workflow-prior-determination-predictive-checks-and-sensitivity-analyses/

#statstab #465 How to embrace variation and accept
uncertainty in linguistic and
psycholinguistic data analysis

Thoughts: An accessible paper on communicating your results with nuance.

#bayes #bayesian #uncertainty #error #bias #guide #tutorial

https://sites.stat.columbia.edu/gelman/research/published/Uncertainty.pdf

#statstab #464 Plotting p-check interaction {brms}

Thoughts: Annoyingly #brms doesn't natively allow plotting for interactions (that I know of). The forum has a solution.

#ppc #posterior #bayesian #modelfit #diagnostic #rstats #r #stan

https://discourse.mc-stan.org/t/plotting-pp-check-interactions/31936

#statstab #463 {modelbased} Understanding your models

Thoughts: A deceptively simple case study on how to understand and report your model.

#rstats #modelling #easystats #r #reporting

https://easystats.github.io/modelbased/articles/workflow_modelbased.html

#statstab #462 Factorial Plots

Thoughts: Not the most modern of plots, but nice to have a guide on what to show based on your design.

#plots #dataviz #ANOVA #interaction

https://www.processma.com/resource/factorial_plots.php

#statstab #461 Interpreting Ordinal and Disordinal interactions

Thoughts: Interactions are not simple things. Their shape can determine many things (including sample size and effect size)

#design #ANOVA #interaction #effectsize #ordinal #crossover

https://www.jolley-mitchell.com/Appendix/WebAppOrdinalInteraction/WebAppOrdinalInteractions.htm