Effects mean different things to different people. What is a big deal to you may not be a big deal to me and vice versa. The interpretation of effect sizes inevitably involves a value judgment and this makes researchers uncomfortable. We prefer cold objectivity to the warm-fuzzies of subjective interpretation.
But if you don’t interpret your own findings, who will?
Failing to interpret your study’s findings is like quitting a marathon a quarter mile short of the finish line. You’ve done all the work—you’ve designed and executed a study—now tell us what you found and what it means.
The wrong way to do this is to look at p values. The right way is to look at the effect size. Is it big or small? In comparison with what? Is it big enough to be meaningful? How does it compare with what others have found?
For more on the interpretation challenge, and to see some worked examples, check out my book Effect Size Matters…