# Why You Shouldn't Conclude "No Effect" from Statistically Insignificant Slopes (repost)

*The following is not a Stats Make Me Cry original, but rather something I came across and found very interesting. If you are interested in the topic, please read the preview and follow the link that follows to the original site.*

*It is quite common in political science for researchers to run statistical models, find that a coefficient for a variable is not statistically significant, and then claim that the variable "has no effect." This is equivalent to proposing a research hypothesis, failing to reject the null, and then claiming that the null hypothesis is true (or discussing results as though the null hypothesis is true). This is a terrible idea. Even if you believe the null, you shouldn't use p > 0.05 as evidence for your claim. In this post, I illustrate why.To demonstrate why analysts should not conclude "no effect" from insignificant coefficients, I return to a debate waged over blogs and Twitter about a NYT article. See Seth Masket's original take, my response, and Seth's recasting. The data come from Nate Silver's post, which adopts a more nuanced position that I think is appropriate in light of the data. *