Friday, January 3, 2014

Common Errors People Make in their Research: Power

About 80% of what crosses my desk for editing contains either no power analysis or the wrong type of power analysis.

What's power? Put simply, it's your odds of finding something that's actually there.

Why should you publish it? Because if you have found nothing in your analysis (p is too high for you to claim that you have a statistically significant finding), then you can evaluate the power and either

  • If power was low, explain that, due to a low N, you may have missed something that is actually there
  • If you had low power in your research, use this point in the discussion section to call for more research into your areaespecially if p was less than, say, .25.
  • If power was high, fortify your claim to have found something important.
Does power relate to the entire study or to a particular analysis? The latter.

How do I figure out power before I begin? Do an a priori power analysis.

How do I figure it out after I've finished? Not surprisingly, do a post hoc power analysis?

What the heck does that mean? "A priori" = "before." "Post hoc" = "after."

How are they different?  Usually if you are doing an a priori analysis you are trying to figure out how many subjects you need. And usually if you are doing a post hoc analysis you are trying to figure out, given the number of subjects you had, how much power you had. I usually include both.

What's the best way to do a power analysis? Download G*Power. It's my favorite tool for power analysis. 

As always, if you need help with your power analysis, contact us at 802-382-7349 or visit us online at