Examples of **effect sizes **are all around us. Consider the following claims which you might find advertised in your newspaper:

– “enjoy immediate pain relief through acupuncture”

– “change service providers now and save 30%”

– “look 10 years younger with Botox”

Notice how each claim promises an effect (“look younger with Botox”) of measureable size (“10 years younger”). No understanding of statistical significance is necessary to gauge the merits of each claim. Each effect is being promoted as if it were intrinsically meaningful. (Whether it is or not is up to the newspaper reader to decide.)

Many of our daily decisions are based on some analysis of effect size. We sign up for courses that we believe will enhance our career prospects. We cut back on carbohydrates to lose weight. We stop at red lights to reduce the risk of accidents. We buy stock we believe will appreciate in value. We take an umbrella if we perceive a high chance of rain.

The interpretation of effect sizes is how we make sense of the world.

In this sense researchers are no different from anybody else. Where researchers do differ is in the care taken to generate accurate effect size estimates. But while we may spend a lot of our time looking for ways to reduce sampling and measurement error, among other things, ultimately our goal is a better understanding of real world effects.

And this is why it is essential that we interpret not only the statistical significance of our results but their real world or substantive significance as well.

For more on how to do this, check out the e-book* Effect Size Matters.
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