#statstab #157 Effect size measures in a two-independent-samples case with nonnormal and nonhomogeneous data
Thoughts: You can never have enough (confusing) effect size measure. At least make them appropriate for your data.
#effectsize #statistics #nonparametric #es #estimation
https://link.springer.com/article/10.3758/s13428-015-0667-z
SpringerLinkEffect size measures in a two-independent-samples case with nonnormal and nonhomogeneous data - Behavior Research MethodsIn psychological science, the “new statistics” refer to the new statistical practices that focus on effect size (ES) evaluation instead of conventional null-hypothesis significance testing (Cumming, Psychological Science, 25, 7–29, 2014). In a two-independent-samples scenario, Cohen’s (1988) standardized mean difference (d) is the most popular ES, but its accuracy relies on two assumptions: normality and homogeneity of variances. Five other ESs—the unscaled robust d (d r * ; Hogarty & Kromrey, 2001), scaled robust d (d r ; Algina, Keselman, & Penfield, Psychological Methods, 10, 317–328, 2005), point-biserial correlation (r pb ; McGrath & Meyer, Psychological Methods, 11, 386–401, 2006), common-language ES (CL; Cliff, Psychological Bulletin, 114, 494–509, 1993), and nonparametric estimator for CL (A w ; Ruscio, Psychological Methods, 13, 19–30, 2008)—may be robust to violations of these assumptions, but no study has systematically evaluated their performance. Thus, in this simulation study the performance of these six ESs was examined across five factors: data distribution, sample, base rate, variance ratio, and sample size. The results showed that A w and d r were generally robust to these violations, and A w slightly outperformed d r . Implications for the use of A w and d r in real-world research are discussed.
Greg Cocks 
![Photos taken horizontally and vertically from the interior of the study area [of trees and undergrowth]](https://files.techhub.social/media_attachments/files/113/671/348/862/639/211/original/78accba6c6f9dbce.jpg "Photos taken horizontally and vertically from the interior of the study area [of trees and undergrowth]")
Excited to win Best Presentation Award at 
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