Every little helps : an investigation of frequency biases in comparative judgments
- Publication date
- Publisher
Abstract
Intuitive statistical inferential judgments involve the estimation of statistical
properties of samples of information, such as the mean or variance. Prior research
has shown that human judges are generally good at making unbiased estimates of
sample properties. However, a series of recent applied consumer research
experiments demonstrated a systematic bias in comparative judgments of item
distributions in which the individual items are paired across those distributions, for
example comparing the prices in two stores selling the same items. When the two
distributions have the same mean, the distribution with the higher number of items
that are smaller in magnitude than the equivalent item in the other distribution is
typically judged to be the smaller of the two distributions: a frequency bias. In a
series of experiments, the research in this thesis provides a robust demonstration of
the frequency bias and explores possible explanations for the bias. A comparison
between simultaneous and sequential presentation of information demonstrates that
the frequency bias cannot solely be explained by the salience of the frequency cue.
A novel web-based experiment, in which information was sampled incidentally from
the environment and a naturalistic task was used to elicit comparative judgments,
showed that the frequency effect persists in an ecologically-valid context. A
systematic comparison between alternative cognitive models of the judgment process
supports an explanation in which items are recalled from memory and compared in a
pair-wise fashion, meaning the frequency bias may be found in a wide range of other
judgment tasks and domains, which would have significant implications for our
understanding of intuitive comparative judgments