#statstab #490 Effect size for difference between variances / Levene's test
Thoughts: Always found it odd that we ignore heterogeneity for inference. We treat it like a "error".
#heterogeneity #welch #levene #variance #cvr #standarddeviation
#statstab #490 Effect size for difference between variances / Levene's test
Thoughts: Always found it odd that we ignore heterogeneity for inference. We treat it like a "error".
#heterogeneity #welch #levene #variance #cvr #standarddeviation
#statstab #489 On the performance of the Neyman Allocation with small pilots
Thoughts: If you know your treatment condition will have larger variance you can optimise your sample size.
#nhst #samplesize #neynan #heterogeneity #welch #variance #pilot #se
https://www.sciencedirect.com/science/article/pii/S0304407624001398
#statstab #488 Why Hedgesβ G*S Based On The Non-Pooled Standard Deviation Should Be Reported With Welchβs T-Test
Thoughts: If you use Welch's t, you must report Hedge's G_av. I think {effectsize} has it.
Henry David Thoreau (1817-1862) American philosopher and writer
Walden; or, Life in the Woods, ch. 18 βConclusionβ (1854)
More about this quote: wist.info/thoreau-henry-david/β¦
#quote #quotes #quotation #qotd #thoreau #henrydavidthoreau #differentdrummer #dissident #eccentricity #heterogeneity #independence #individualism #individuality #inspiration #meme #nonconformist #pace #selfdirection #tempo #time #unconventionality #uniqueness #difference
#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 #452 Assumption Checking Prevents Assumption Hacking in Multiverse Meta-Analyses
Thoughts: Heterogeneity, publication bias, multiverse analysis, z-curve and more.
#zcurve #multiverse #heterogeneity #metaanalysis #bias #publicationbias
In a nutshell Statistical methods for meta-analyses make different assumptions. For this reason, different methods can produce different results with the same data. Meta-analysts often struggle to make sense of these inconsistent results. A simple solution to this problem is to test testable assumptions. Key assumptions that influence results are publication bias and heterogeneity of
#statstab #448 {metaforest} Small sample meta-analysis
Thoughts: "a machine-learning based, exploratory approach to identify relevant moderators in meta-analysis"
#ML #MachineLearning #metaanalysis #smallsample #samplesize #heterogeneity #moderator
https://cjvanlissa.github.io/metaforest/articles/Introduction_to_metaforest.html
#statstab #448 {metaforest} Small sample meta-analysis
Thoughts: "a machine-learning based, exploratory approach to identify relevant moderators in meta-analysis"
#ML #MachineLearning #metaanalysis #smallsample #samplesize #heterogeneity #moderator
https://cjvanlissa.github.io/metaforest/articles/Introduction_to_metaforest.html