Hi all,

Just a quick question (as a newcomer to bioinformatics) regarding effect size in differential expression analysis. Why does the field opt for using fold change as a metric of effect size? Fold change doesn't take into account variability, whereas standardized effect size measures like Cohen's d do. So why doesn't the field report effect sizes that take into account variability?

To illustrate an example, say gene X has a mean of 7.20 in condition A and 7.60 in condition B. Fold change for condition B compared to condition A is 7.60/7.20 = 1.05. Say the standard deviation estimates on condition A is 0.09, while in condition B its 0.10. Computing Cohen's d on this, the effect size is somewhere around 4.2, which is a gigantic effect. Fold change and Cohen's d differ dramatically, so why not report effect size estimates that take into account variability rather than fold change?

Thanks,

Mike

Hi Devon,

Thanks for the quick answer on this. I would agree that confidence intervals are quite useful pieces of information and would probably go well with something like Cohen's d. And couldn't agree more when it comes to p-values.

Mike