When two things are related, one might be tempted to see a causal path. For instance, that mental health problems lead to unemployment. It is possible that it is reversed, such as unemployment, causing mental health issues.

When you think about the association, ask if...

Generalizations about how two groups differ in some way often draw on stereotypes while ignoring the similarities.

Asking for the "effect size" can prevent this error. It is a measure of how much the average of one group differs from the average of another.

We sometimes forget to consider "third factors," or outside causes, that could be the link between two things, because both are actually effects of the third factor.

Avoid this error by always considering more factors when you see a correlation.

A graph maker may choose a small range of a larger graph to highlight a little difference or association and make it look more significant.

Take care to note the graph's labels along the axes. Question unlabelled graphs.

Just because two things change at the same time, or in similar ways, does not mean they are related.

Question the observed association. Are there many occurrences, or is this merely chance? Can you predict future associations?

A focus on a "normal distribution," - also known as a "bell curve," is where most people are near the average score, and only a small group is far above or below average. However, when you're dealing with extremes, small group differences can matter a lot.

Although a small change in perfo...