A good representation can be crucial for finding the solution to a problem. Gigerenzer and<br>Hoffrage (Psychol. Rev. 102 (1995) 684; Psychol. Rev. 106 (1999) 425) have shown that representations<br>in terms of natural frequencies, rather than conditional probabilities, facilitate the computation<br>of a cause's probability (or frequency) given an effect - a problem that is usually referred to as<br>Bayesian reasoning. They also have shown that normalized frequencies - which are not natural<br>frequencies - do not lead to computational facilitation, and consequently, do not enhance people's<br>performance. Here, we correct two misconceptions propagated in recent work (Cognition 77 (2000)<br>197; Cognition 78 (2001) 247; Psychol. Rev. 106 (1999) 62; Organ. Behav. Hum. Decision Process.<br>82 (2000) 217): normalized frequencies have been mistaken for natural frequencies and, as a<br>consequence, "nested sets" and the "subset principle" have been proposed as new explanations.<br>These new terms, however, are nothing more than vague labels for the basic properties of natural<br>frequencies.
The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education