17.1: Inferential Heuristics

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Human beings have many limitations. We have limited memories, attention spans, and computational abilities. We also have better things to do than to spend our time trying to reason precisely about everything that we ever think about. So, we use shortcuts. These shortcuts are called inferential (or judgmental) heuristics.

An inferential (or judgmental) heuristic is a general strategy that we use for drawing inferences. It is a rough-and-ready device, a cognitive shortcut, a rule of thumb for reasoning. We use inferential heuristics frequently, usually without being aware of it. Heuristics are usefully contrasted with definite and specific rules for reasoning (like our earlier rules for calculating probabilities).

Like many of the cognitive mechanisms studied in earlier chapters, inferential heuristics are often quite useful. They allow us to draw rapid inferences without having to gather data or compute probabilities. This is valuable, because we rarely have the time, energy, know-how, or interest to go to such trouble. Indeed, to survive, organisms must be able to process information and draw conclusions quickly, and handy, habitual rules of thumb—heuristics—are often better suited for this than more reliable, but time-consuming, rules for reasoning. The drawback is that overreliance on inferential heuristics can lead to serious biases or errors in reasoning.

Sampling Revisited

Remember how inferences from samples to populations work. When we infer a conclusion about a population from a description of a sample from it:

The premises are claims about the sample.
The conclusion is a claim about the population.

For example, we might draw a conclusion about the average income of Oklahomans based on the results of a sample of 2,000 Oklahomans.

Our conclusion involves an inductive leap. It goes beyond the information in its premises, because it contains information about the entire population, while the premises only contain information about the sample. But if we satisfy two conditions involving the sample, our inference can still be inductively strong. We must have:

A large enough sample.
A representative (unbiased) sample.

An unbiased sample is typical of the population. By contrast, in a biased sample, some portions of the population are overrepresented and others are underrepresented. The problem with a very small sample is that it unlikely to be representative. Other things being equal, a bigger sample will be more representative. But there are costs to gathering information, costs in time, dollars, and energy, so it is rarely feasible or desirable to get samples that are huge.