*[Excerpts taken from the blog, “No, average statistical power is not as high as you think: Tracing a statistical error as it spreads through the literature”, by Andrew Gelman, posted at *__Statistical Modelling__]

__Statistical Modelling__]

###### “I was reading **this** recently published article by Sakaluk et al. and came across a striking claim:”

**this**

*“Despite recommendations that studies be conducted with 80% power for the expected effect size, recent reviews have found that the average social science study possesses only a 44% chance of detecting an existing medium-sized true effect (Szucs & Ioannidis, 2017).”*

###### “I noticed this not because the claimed 44% was so low but because it was so high! I strongly doubt that the average social science study possesses a power of anything close to 44%. Why? Because 44% is close to 50%, and a study will have power of 50% if the true effect is 2 standard errors away from zero. I doubt that typical studies have such large effects.”

###### “…if researchers come into a study with the seemingly humble expectation of 44% power, then they’ll expect that they’ll get “p less than 0.05” about half the time, and if they don’t they’ll think that something went wrong. Actually, though, the only way that researchers have been having such a high apparent success rate in the past is from forking paths. The expectation of 44% power has **bad consequences**.”

**bad consequences**

###### To read the full blog, **click here**.

**click here**