A Bayesian view of covariation assessment.

When participants assess the relationship between two variables, each with levels of presence and absence, the two most robust phenomena are that: (a) observing the joint presence of the variables has the largest impact on judgment and observing joint absence has the smallest impact, and (b) participants' prior beliefs about the variables' relationship influence judgment. Both phenomena represent departures from the traditional normative model (the phi coefficient or related measures) and have therefore been interpreted as systematic errors. However, both phenomena are consistent with a Bayesian approach to the task.

Do Conditional Hypotheses Target Rare Events?

When testing hypotheses, rare or unexpected observations are normatively more informative than common observations, and recent studies have shown that participants' behavior reflects this principle. Research has also shown that, when asked to test conditional hypotheses ("If X, then Y") that are abstract or unfamiliar, participants overwhelmingly consider a supporting observation mentioned in the hypothesis (X&Y) to be more informative than a supporting observation not mentioned ( approximately X approximately Y). These two empirical findings would mesh well if conditional hypotheses tend to be phrased in terms of rare, rather than common, events.