Latest posts tagged with #PValues on Bluesky
The letter "p" is repeated multiple times on a blank background. All but one of the letters are gray; one "p" in the center is distinctive because it is red.
In our latest blog post, we outline two approaches to reporting significant p values in tables and figures.
๐ apastyle.apa.org/blog/methods...
#Statistics #BehavioralStatistics #AcWri #APAStyleTables #APAStyleFigures #pValues
#statstab #455 False Discovery Rate (FDR) and q-values
Thoughts: The q-value of a test is the expected proportion of false positives among all hypotheses with p-values as small or smaller than that test.
#pvalues #qvalues #FDR #FWER #error
www.nonlinear.com/support/prog...
#FactCheck Friday! P values depend on the effect size, not the sample size. FALSE! They depend on both, and it's not unusual for studies with large sample sizes to yield small #Pvalues even when the #EffectSize is so small it's clinically irrelevant. institutionalrepository.aah.org/jpcrr/vol10/...
#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
jaromilfrossard.github.io/permuco/inde...
#statstab #449 Significance tests, p-values, and falsificationism
Thoughts: A statistician and a philosopher debate p-values (not the setup to a joke). Good thread.
#pvalues #significance #nhst #fisher #greenland #epistemology #statistics
discourse.datamethods.org/t/significan...
Enlightened monks. Crying babies. Data scientists
๐ All bound by the same trauma: p-values
The too-high one
The model that made no sense
The sketchy dataset you used anyway
Bayes wonโt absolve youโbut it will help you understand
๐ Step into the Bayesian Booth. Confess.
#BayesianMethods #pvalues
#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/23...
#FactCheck Friday! P values depend on the effect size, not the sample size. FALSE! They depend on both, and it's not unusual for studies with large sample sizes to yield small #Pvalues even when the #EffectSize is so small it's clinically irrelevant. institutionalrepository.aah.org/jpcrr/vol10/...
โA big winโ: Dubious statistical results are becoming less common in psychology: www.science.org/content/arti...
#Science #Psychology #ScholarlyPublishing #AcademicPublishing #AcademicSky #AcademicPublishing #AcademicChatter #Data #PValues
๐ ๐งช CPH Focus: Decode the meaning of p-values & statistical significance, key to interpreting study results in public health research. Clarify your knowledge with clear explanations and practice questions here: buff.ly/KS5xH11 #EpiSky #MedSky #Biostatistics #pValues #CPH #PublicHealth
#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
doi.org/10.1016/j.hp...
#statstab #337 Confidence intervals and tests are two sides of the same research question
Thoughts: Comment describing the connection between NHST p-values/test and Confidence Intervals (CI).
#NHST #ConfidenceIntervals #pvalues #frequentist #estimation
doi.org/10.3389/fpsy...
#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
doi.org/10.1016/j.jm...
This #editorial offers guidance for medical researchers on the proper use & interpretation of #Pvalues and provides a broader discussion about effect size estimates, confidence intervals, & clinical implications when interpreting #quantitative results. institutionalrepository.aah.org/jpcrr/vol10/...
#statstab #300 (!!!) ๐ฅณ๐
Beyond the forest plot: The drapery plot
Thoughts: I can't believe they didn't call these 'tepee plots'.
I think they are cluttered, but can be useful.
#metaanalysis #pvalues #consonancecurve #pvaluefunction #dataviz #prediction #plots #figures #R
doi.org/10.1002/jrsm...
#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
physoc.onlinelibrary.wiley.com/doi/10.1113/...
#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
doi.org/10.3389/fpsy...
#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
youtu.be/5OL1RqHrZQ8
#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
statmodeling.stat.columbia.edu/2023/04/14/4...
#statstab #255 The p-value and model specification in statistics
Thoughts: Little thought is given to the appropriateness of the test and its assumptions.
#pvalues #nhst #nullhypothesis #testing #ttest #fisher
gpsych.bmj.com/content/32/3...
If you are teaching an introduction to statistics course this spring and are looking for a fun way to illustrate p-values to your students, I've found this to be effective for undergrad crowds. #statistics #education #pvalues
DESeq2 P-Value Histogram
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 #234 Not all alphas can be justified
Thoughts: When using discrete distributions, with few outcomes, your alpha cannot always take the values you want. Same with the distribution of p-values under the null.
#pvalues #nhst #errorcontrol #alpha
osf.io/preprints/ps...
Ha! The infamous p < 0.05? Fisher picked it as a simple, flexible thresholdโnot a universal rule. Journals turned it into dogma, ignoring context & effect sizes. Fisher even warned against this; he viewed p-values as a continuum of evidence. So โ #StatsSky #pvalues
Large samples can make trivial effects look significant (e.g., p=0.001 for a tiny 0.01% blood pressure change). Small samples can miss meaningful effects due to low power. P-values โ importanceโcontext and effect size matter! #Statssky #pvalues ๐
#statstab #217 The distribution of p-values obtained in replications depends only on the original p-value. How can it be true?
Thoughts: A great discussion where the author chimes in to explain the issue.
#replication #pvalues #confidenceinvervals #bayes
stats.stackexchange.com/questions/25...
![An awareness of the history of p-values might elp deflate their swollen stature and encourage more judicious use. We were surprised to learn, in the course of writing this article, that the p < 0.05 cutoff was established as a competitive response to a disagreement over book royalties between two foundational statisticians. In the early 1920s, Kendall Pearson, whose income depended on the sale of extensive statistical tables, was unwilling to allow Ronald A. Fisher to use them in his new book. To work around this barrier, Fisher created a method of inference based on only two values: p-values of 0.05 and 0.01 (Hurlbert and Lombardi,
2009). Fisher himself later admitted that Pearson's more continuous method of inference was better than his binary approach: "no scientific worker has a fixed level of significance at which from year to year, and in all circumstances, he rejects [null] hypotheses; he rather gives his mind to each particular case in the light of his evidence and ideas (Hurlbert and Lombardi, 2009: 316). A fair interpretation of this history is that we use p-values at east in part because a statistician from the 1920s was afraid that sharing his work would undermine his income](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:iljjw2bkuvcrugonpazt6p3g/bafkreihpmiazec7nstrszzzmbuzs4aof62jr7j6ujfvr3sykokibsdrd3u)
An awareness of the history of p-values might elp deflate their swollen stature and encourage more judicious use. We were surprised to learn, in the course of writing this article, that the p < 0.05 cutoff was established as a competitive response to a disagreement over book royalties between two foundational statisticians. In the early 1920s, Kendall Pearson, whose income depended on the sale of extensive statistical tables, was unwilling to allow Ronald A. Fisher to use them in his new book. To work around this barrier, Fisher created a method of inference based on only two values: p-values of 0.05 and 0.01 (Hurlbert and Lombardi, 2009). Fisher himself later admitted that Pearson's more continuous method of inference was better than his binary approach: "no scientific worker has a fixed level of significance at which from year to year, and in all circumstances, he rejects [null] hypotheses; he rather gives his mind to each particular case in the light of his evidence and ideas (Hurlbert and Lombardi, 2009: 316). A fair interpretation of this history is that we use p-values at east in part because a statistician from the 1920s was afraid that sharing his work would undermine his income
#statstab #209 Limitations of empirical calibration of p-values using observational data
Thoughts: Obs research doesn't need p-values but ppl keep tryin to make'em happen
#pvalues #observational #empirical #causal
pmc.ncbi.nlm.nih.gov/articles/PMC...
rebuttal
pubmed.ncbi.nlm.nih.gov/27592566/
