Top Tweets for #PValues
#statstab #542 Improving the utility of non-significant results for educational research
Thoughts: Another paper that tries to clarify some issue with NHST.
#nhst #stats #pvalues #education #frequentist #misconceptions
https://t.co/LXfrmVQ6hL
#statstab #460 {permuco} permutation tests in linear models with nuisances variables
Thoughts: Supports ANOVA, ANCOVA, t-tests and more.
#permutation #randomization #ANOVA #rstats #r #pvalues #ancova #ttest
https://t.co/AXy9XeY8u5
If you find free P-values on the street, take what you need and leave the tiny ones for the stats nerds in need #PValues #Statistics
#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
https://t.co/AVDBEMUALG
#statstab #359 A Pragmatic Approach to Statistical Testing and Estimation (PASTE)
Thought: A (basic) guide to some alternatives to p-values: bayesian posterior intervals, Bayes Factors, and AIC.
#NHST #pvalues #TOST #BayesFactor #AIC #modelcomparison
https://t.co/xHBKfCGWgF
#statstab #309 The statistical significance filter leads to overoptimistic expectations of replicability
Thoughts: Not sure how many researchers interpret p-values are indexes of replicability, but they shouldn't.
#replication #pvalues #TypeMerror #meta
https://t.co/m5VJmfIaTE
This #editorial offers guidance for medical researchers on the proper use and interpretation of #Pvalues and provides a broader discussion about effect size estimates, confidence intervals, and clinical implications when interpreting #quantitative results. https://t.co/5Epj5IDlMZ
#statstab #298 Replication: Do not trust your p-value, be it small or large
Thoughts: Even under exact replications, a p-values is not very good at predicting the p-value in a future study. p=.05 ~ 50% rep.
#pvalues #replication #estimation #metascience
https://t.co/UToMx3bvQy
#statstab #289 The meaning of significance in data testing
Thoughts: Fisherian significance testing =/= Neyman-Pearson statistical hypothesis testing. Many debates on p-values and frequentist stats are due to this confusion.
#pvalues #NHST #frequentist
https://t.co/RWbIBs5rUp
#statstab #287 Dance of the p Values
Thoughts: One of my go-to demonstrations for the variability of p-values, and why they say so little about a study.
#pvalues #NHST #education #estimation #frequentist #replication #error #visualization #teaching
https://t.co/LJlOkUPHti
#statstab #272 Different meanings of p-values
Thoughts: A riveting (& confusing) discussion on the definitions & properties of p-values. W/ guest appearance from some big names in stats, from all camps.
#NHST #pvalues #divergence #bayes #compatibility
https://t.co/RQDw0fRkVk
The p-value histogram shows a strong peak near zero, confirming that many genes are significantly differentially expressed in response to HOCl treatment.
#Bioinformatics #RNAseq #PValues
#statstab #209 Limitations of empirical calibration of p-values using observational data
Thoughts: Obs. research doesn't need p-values (imo) but ppl keep tryin to make'em happen
#pvalues #observational #empirical #causal
https://t.co/jYAt30tkLf
rebuttal
https://t.co/lhC9zsYmfz
#statstab #156 Exact and asymptotic p-values
Thoughts: An important stats lesson is to be specific with terms. Exact p-values are not the same as asymptotic p-values (what most tests report w/o specifying).
#pvalues #nhst #permutation #randomization
https://t.co/ndJvrsxg2v
#PSA: if your study doesn't have 1. a random sample, and 2. randomization to the experimental condition, then using a p-value for your decision-making metric is nonsense.
#pvalues #causalinference #qrp #observationalresearch
#statstab #134 A New Look at P Values for Randomized Clinical Trials
Thoughts: For obs research, empirically calibrated p-values might be the only way forward. But should we have them per field also?
#medicine #health #pvalues #datascience #statistics
https://t.co/5YyAbuZUiS
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