# Evidence-based measures of effect

## Use the empirical literature to your advantage

**conduct an**. Doing so will tell you

*a priori*power analysis**how many people that you will need in your sample size to detect the effect size or treatment effect in your study**.

Without an

*a priori*calculation, you could frivolously

**waste**months or years of your life conducting a study only to find out that you only needed 100 in each group to achieve significance. Or, with the inverse, you conduct a study with only 50 patients and find out in a post hoc fashion that you would have needed 10,000 to prove your effect!

If you are using

__and__

**Research Engineer****G*Power**to run your analyses, here are the things you will need:

1.

**An evidence-based measure of effect from the literature is the first thing you should seek out**. Find a study that is

**theoretically, conceptually, or clinically**similar to your own. Try to find a study that

**uses the same outcome you plan to use in your study**.

2. Use the

**means, standard deviations, and proportions**from these published studies as evidence-based measures of effect size to calculate how large of a sample size you will need. These values will be

**reported in body of the results section or in tables within the manuscript**. It shows more

**empirical rigor**on your part if you conduct an

*a priori*power analysis based on a well-known study in the field.

3. Plug these values into

**G*Power**using the steps published on the

__page to find out how many people you will need to collect for your study.__

**sample size**