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
<p>Historical inertia mostly.</p>

<p>Having said that, there&#39;s a slowly growing movement within bio-related fields to include confidence intervals in all estimate reports. While those obviously aren&#39;t standardized effect sizes, they serve a similar purpose (e.g., you can use them for power calculations, which more people should actually do). Actually, I find confidence intervals more useful than standardized effect sizes, but perhaps that&#39;s just me.</p>

<p>I should add that standardized effect sizes are not a panacea. My original background is ion channel biophysics, where we can robustly measure very very very very small changes in an extremely robust manner. I could trivially get Cohen&#39;s D values vastly larger than the one you showed, but the results would still be biologically meaningless. That&#39;s the thing with relying too much on any single number (this goes doubly for p-values), they&#39;re easy to point at and yell, &quot;This is important!&quot;, when a finding isn&#39;t actually.</p>
