While most research studies use statistical significance to reach their conclusions, clinical research studies should report on the “effectiveness” of a study. Statistical significance is of limited value when we want to determine if the treatment will have a clinical benefit.

For clinicians, the most fundamental question of clinical significance is usually, “Is the treatment effective, and will it change my practice?” The **effect size** is one of the most important indicators of clinical significance. It reflects the magnitude of the difference between treatment groups; a greater effect size indicates a larger difference between experimental and control groups. For example, if the experimental control group improves by 15 points, and the control group improves by 10 points, the change score is 5.

Cohen established effect values based on group differences (change score), divided by the combined standard deviation:

Change in Experimental Group vs. control / Combined Standard Deviation of both groups

For example, if the difference between groups (change score) is 5, and the standard deviation of both groups is 10, the Cohen score (effect size) = 0.5.

Cohen quantified effect sizes in ranges, which may be positive or negative, indicating the direction of the effect:

<0.2 = trivial effect

0.2-0.5 = small effect

0.5-0.8 = moderate effect

> 0.8 = large effect