Consumer predictions: Do categories matter when predicting the lottery or stock market?
From sports to the stock market and even winning the lottery, it's in our nature to predict who or what will come out on top. But, sometimes we can't see the forest for the trees. According to a new study in the Journal of Consumer Research, people are more likely to make a prediction about something when it is grouped in a large category of similar items.
"One factor that can contribute to a person's flawed judgment is categorization," write authors Mathew S. Isaac (Seattle University) and Aaron R. Brough (Utah State University). "When making a prediction, we can become distracted by how all of the various possibilities are grouped. The basic question of our research is, 'Can the size of the category make an outcome seem more or less likely to occur?'"
In a series of five experiments, the authors investigated how changes in category size affected judgments about probability. In one experiment, each participant was given a lottery ticket. Lottery ticket colors varied–most of the distributed tickets were blue while some tickets were yellow. Participants were asked to write their names on the back of their ticket and indicate whether they would wager additional money on winning the overall lottery. Despite an equal probability of any ticket being drawn, participants holding blue tickets were willing to wager an average of 25% more money than the participants holding yellow tickets.
Offering insight on how category size can impact a person's perception of risk and probability, study results can help businesses and policy makers better communicate risk-related information. For example, when crafting health-related messages, grouping a highly preventable disease such as lung cancer with a large group of other potential health risks could increase the perceived risk of contracting lung cancer and, in turn, persuade people to visit the doctor for regular screenings.
"While organizing our world into groups or categories is an incredibly efficient way to process complex information, we sometimes have to focus on the individual outcome that we are trying to predict," the authors conclude.