what does high effect size variance mean

Is it appropriate to use the term “effect size” to mean things like unstandardized mean differences?.

If I recall correctly, the reasons were: 1) statisticians do not use the term “effect size” in that broad sense, 2) unstandardized mean differences are problematic, and calling them “effect sizes” may encourage HCI researchers to abuse them. At the CHI meeting, a participant who was manifestly knowledgeable about effect sizes strongly objected to this. The guidelines currently use “effect size” in a broad sense, and often mentions unstandardized mean differences as an example. This includes simple and familiar metrics like unstandardized mean differences, e.g., a mean difference in completion time between two techniques, expressed in seconds. Briefly, the narrow sense refers to a family of standardized (i.e., unitless) measures such as Cohen’s d, while the broad sense refers to any measure of interest, standardized or not. The current draft of the transparent statistics guidelines ( section 2.1, Effect Size FAQ) mentions that there is a narrow sense and a broad sense of the term “effect size”. Unfortunately, methodologists rarely explain what they exactly mean by “effect size”.

Evidence is thought of as being continuous rather than some sort of dichotomy. Attention is paid to effect sizes and confidence intervals. In the post p<0.05 era, scientific argumentation is not based on whether a p-value is small enough or not. For example, Ron Wasserstein, the executive director of the American Statistical Association, stated that:

We are repeatedly told that effect sizes are important, and many methodologists urge us to report effect sizes.

What does high effect size variance mean