# Explicit Predictions for Illness Statistics

- Talia Robbins,
*Rutgers University*
- Pernille Hemmer,
*Rutgers University*

## Abstract

People’s predictions for real-world events have been shown
to be well-calibrated to the true environmental statistics (e.g. Griffiths and
Tenenbaum 2006). Previous work, however, has focused on predictions for these
events by aggregating across observers, making a single estimate for the total
duration given a current duration. Here, we focus on assessing predictions for
both the mean and form of distributions in the domain of illness duration
prediction at the individual level. We assess understanding for both acute
illnesses for which people might have experience, as well as chronic conditions
for which people are less likely to have knowledge. Our data suggests that for
common acute illnesses people can accurately estimate both the mean and form of
the distribution. For less common acute illnesses and chronic illnesses, people
have a tendency to overestimate the mean duration, but still accurately predict
the distribution form.

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