# Gatekeeper vs Keymaster: the role of statistician / mathematical modeler in science

I think the average researcher views statisticians as a kind of “Gatekeeper” of publication. Do the right incantations, appease the worries about distributional approximations, or robustness of estimators, get the p < 0.05 or you can’t publish. In this view, the statistician doesn’t add to the researcher’s substantive hypothesis, more keeping the researcher from getting into an accident, like a kind of research seat-belt.

The alternative version is what I like to think of as the Keymaster role. A researcher, having a vague substantive hypothesis and an idea of technically how to go about collecting some data that would be relevant, can come to a good statistician, or better yet mathematical-modeler (which encompasses a little more than just applied probability, sampling theory etc) who will help make aÂ vague notion into a fairly precise and quantitative statement about the world. This process will get you thinking about the relationships between your quantities of interest, and identify some substantive but unknown parameters that describe the systemÂ you are studying. That model structureÂ will then give you a sense of what data will best inform you about these precise quantities, and then ultimately when the KeymasterÂ analyzes the collected data, he or she can extract the meaningful internal unobserved quantities that you really care about (but didn’t know about) originally.

This explains why I think it’s a big mistake to go out and collect some data first and then show up and expect a statistican to help you make sense of it.

And, I mean really, who *wouldn’t* want to be Rick Moranis??

Comments are closed.