Research Design

Statistical Modeling: A Primer (by Kevin Gray)

March 7, 2017

Interesting article by Kevin Gray at Cannon Gray (http://cannongray.com) Model means different things to different people and different things at different times. As I briefly explain in A Model’s Many Faces, I often find it helpful to classify models as conceptual, operational or statistical. In this post we’ll have a closer look at the last of these, statistical models. First, it’s critical to understand that statistical models are simplified representations of reality and, to paraphrase the famous words of statistician George Box, they’re all wrong but some of them [READ MORE]

Type I and II errors – Hypothesis testing

March 10, 2015

In so many statistical procedures we execute, statistical significance of findings is the basis of statements, conclusions, and for making important decisions. While the importance of statistical significance (compared with practical significance) should never be overestimated, it is important to understand how statistical significance relates to hypothesis testing. A hypothesis statement is designed to either be disproven or failed to be disproven. (Note that a hypothesis can be disproven (or failed to be disproven), but can not proven to be true). Hypotheses relate to either [READ MORE]