#statstab #314 One-sided significance tests
Thoughts: Directional tests are more complex than would initially seem. Here is one view on their proper use.
#statstab #310 Directional (One-Sided) versus Non-Directional (Two-Sided) Tests
Thoughts: This topic is more nuanced than just power, but useful to know that you save on sample size *if* you're right!
#hypothesis #nhst #power #onetail #directionaltest
https://lakens.github.io/statistical_inferences/05-questions.html#sec-onesided
Imagine you preregistered study with tests A and B for a directed hypothesis H, but did not specify one-tailed or two-tailed testing. Test A is in the predicted direction, but p = between .05 and .09. Test B is in the non-predicted direction, but p < .05. How do you report results?
#statstab #295 The Fallacy of the Null-Hypothesis Significance Test
Thoughts: "the [..] aim of a scientific experiment is not to precipitate decisions, but to make an appropriate adjustment in the degree to which one accepts, or believes, the hypothesis"
#NHST #Bayes #ConfidenceIntervals #pvalues #significance #testing #hypotheses #likelihood #critique #fallacy
#statstab #290 An Evaluation of Four Solutions to the Forking Paths Problem
Thoughts: Multiverse analysis & the forking paths problem must be considered methodologically but also logically: based on one' stat philosophy.
#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.
#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
#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.
#statstab #270 Seeing Theory: Frequentist Inference
Thoughts: Are you learning about NHST? Are you a visual learner? This might be for you.
#NHST #dataviz #visualisation #learning #education #teaching #frequentist
https://seeing-theory.brown.edu/frequentist-inference/index.html
#statstab #233 Conditional equivalence testing (CET rule)
Thoughts: Many might not know that there are several decision rules for equivalence test. This one is slightly different from TOST classic.
#equivalencetests #equivalence #rules #nhst
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0195145
#statstab #230 Power and Sample Size Determination
Thoughts: Frequentist power is a complicated and non-intuitive thing, so it's good to read various tutorials/papers until you find one that sticks.
#stats #poweranalysis #power #NHST #effectsize
https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_power/bs704_power_print.html
#r and #statstodon people: do you have any resources for running sensitivity power analyses for mixed effects models? (e.g. for fixed samples to determine what is the smallest reliable detectable effect at a specific power level)
#statstab #215 Four one-sided test of significance (FOST)
Thoughts: If you liked the "two one-sided t-tests" you'll love four! 2x the fun and inference. Superiority+equivalence+inferiority
#statstab #199 Mixed model equivalence test using R and PANGEA
Thoughts: While there are easier ways to compute #EQ tests for such models now, it is nice to see how you'd do so manually.
#equivalencetests #NHST #mixedeffects #r #stats #nullresults
#statstab #194 Design and analysis of noninferiority studies
Thoughts: Sometimes all you want to know is "is this new thing not worse than the previous one?". Enter noninferiority tests.
