Academic journal article Memory & Cognition

An Encounter Frequency Account of How Experience Affects Likelihood Estimation

Academic journal article Memory & Cognition

An Encounter Frequency Account of How Experience Affects Likelihood Estimation

Article excerpt

When making judgments, people often favor information received from a few individual sources over large-sample statistical data. Individual information is usually acquired piece by piece, whereas statistical information combines many observations into a single summary. We examined whether this difference in the frequency of encounters affects how data are weighted. In two experiments, subjects read statistical information indicating an event to be rare and contrasting information from individual cases suggesting the event to be common. We controlled whether the individual cases were summarized into a single summary like statistical information, or presented serially, case by case. Subjects' estimates of event frequencies were higher when the individual cases were presented in serial, rather than summarized, format. A third study demonstrated that subjects treat each data sample as an instance, and do not weight according to sample size. These results support the conclusion that people weight information according to encounter frequency.

People are constantly faced with uncertainty. We can only estimate how likely we are to catch the flu this season, to experience medication side effects, or to miss the morning train. To judge how likely an event is to occur, information may be recalled from a variety of sources. In this article, we discuss such sources as belonging to one of two categories: statistical or individual. We aim to show that this distinction is useful when predicting how people will judge the likelihood of an event's occurrence.

We start with a familiar result-that is, people's tendency to overlook relevant statistical information. Fong, Krantz, and Nisbett (1986) demonstrated this phenomenon when they asked subjects to make judgments about a number of hypothetical problems in which statistical principles were relevant. For example, subjects read about the Caldwells, a couple trying to decide which of two car brands, Saab or Volvo, is better. Subjects read that both the Consumer Reports car experts and Consumer Reports readers slightly favored Volvos over Saabs. Then they read that the Caldwells have two Saab-owning friends and one Volvo-owning friend. The two Saab owners had few problems with their cars, whereas the Volvo owner describes negative experiences with his car. When asked to evaluate scenarios such as these, fewer than half of subjects' responses included a statistical explanation. Clearly, at least to psychologists, sample size is a relevant concept in this example. The information from Consumer Reports is based on many more people's experiences than that learned from the Caldwells' three friends.

Overlooking sample size when comparing information from statistical and personal sources is related to Nisbett and Ross's (1980) "man who," or, more recently, "person who" (Stanovich, 2001), reasoning. "Person who" reasoning occurs when one discounts statistical information in light of a conflicting personal anecdote. For example, one may doubt a report stating that employment is on the rise, because he knows a "person who" can't find a job. This type of reasoning is problematic, because it runs counter to the law of large numbers. According to both the weak and strong versions of the law, a population mean is generally better approximated by a large, rather than a small, sample from a population. Sedlmeier and Gigerenzer (1997) argued that other mathematical theorems, rather than the law of large numbers, are more appropriate for justifying sample size use as normative. Nevertheless, the overall picture is the same: Sample size is a normatively relevant factor, but it is commonly overlooked. In the Volvo-Saab scenario, there is more reason to believe that the Consumer Reports data provide a reliable estimate of car soundness, because they are based on a larger sample than the information provided by the Caldwells' friends. From this viewpoint, the Fong et al. (1986) subjects should have explained that the Volvo is a better choice than the Saab because there is more evidence to support this hypothesis than the other way around, but they did not. …

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