Association, Cause, and Correlation

Abstract

Anything one measures can become data, but only those data that have meaning can become information. Information is almost always useful; data may or may not be. This chapter will address the various ways one can measure the degree of association between an exposure and an outcome and will include a discussion of relative and absolute risk, odds ratios, number needed to treat, and related measures. In addition, this chapter will introduce the concept of causal inference.

The star of the play is the effect size i.e. what you found

The co-star is the effect size’s confidence interval i.e. the precision that you found

If needed, supporting cast is the adjusted analyses i.e. the exploration of alternative explanations

With a cameo appearance of the p value, which, although its career is fading, insisted upon being included

Do not let the p value or an F statistic or a correlation coefficient steal the show, the effect size must take center stage!

But remember it takes an entire cast to put on a play!