the false consensus effect

the false consensus effect

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Research Demonstration: The False Consensus Effect
In science, we emphasize systematic, careful observation as a key to
overcoming the limits of other methods of acquiring knowledge. That is, we
trust systematic observation more than we trust our own intuition. We can
actually investigate this issue. The following description provides you with
the details necessary to conduct a simple study to investigate the accuracy of
human intuitions.
We often believe that others are more like ourselves than they really are. Thus, our
predictions about others' beliefs or behaviors, based on casual observation, are very likely
to err in the direction of our own beliefs or behavior. For example, college students who
preferred brown bread estimated that over 50% of all other college students preferred
brown bread, while white-bread eaters estimated more accurately that 37% showed brown
bread preference (Ross, Greene, & House, 1977). This is known as the false consensus
effect (Ross et al., 1977; Mullen, Atkins, Champion, Edwards, Hardy, Story, & Vanderlok,
1985). The false consensus effect provides the basis for the following demonstration, which
emphasizes the need for systematic rather than casual observation.
You can use the set of six questions, below, to investigate this. Before describing the false
consensus effect, have friends, roommates or classmates (other classes, not PSY250)
answer the questions listed below. Next, have students predict the UB undergraduate
mean for each question. Keep a record of the responses for each person who participates.
According to the false consensus effect, students' predictions about the UB mean should be
influenced by their own positions. Consequently, a student whose position is below the UB
mean is likely to make a prediction that will be below the UB mean as well.
There are ethical constraints on the use of human participants that you must follow if you
wish to try this with people.
1. Do not collect any identifying information on your participants. The answers to
these questions should be anonymous. Even though you may know the person, do NOT
record any identifying information.
2. When you ask someone to participate, explain the basic nature of the study. You
want to ask people how often they do certain things, like laundry, and how often they think
other UB undergrads do these same things. You are doing this as part of a class on learning
the scientific method. If they participate, they will be asked to answer six questions about
themselves and other UB undergraduates. They can choose not to answer any question.
Tell your prospective participant that all answers are anonymous and no information
identifying them is being recorded.
3.

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If they indicate that they are willing, ask the questions or let them read them.
You can do this either way. The questions appear below on a separate page for your use.
4. Afterward, thank your participant. Explain to them that your study is looking at
how people estimate how often others do various tasks and whether these estimates are
influenced by how often they do these same tasks.
To demonstrate the effect statistically, compute the UB mean for each question using the
students' personal data. Unless you do this with a lot of people, your data may not
accurately represent UB undergrads. The easiest way to get around this is to pool your data
with those collected by others in the class. Next, compute a score for each participant in
the following way: For each question, score a +1 if the participant's personal answer and
predicted UB mean are either both below or both above the actual UB mean; score a -1 if
the participant's personal score and predicted class mean are on opposite sides of the actual
UB mean. Sum all six questions so that each participant now has a single score that ranges
between -6 and +6. If people err randomly, the average score for all students should be
zero. In contrast, if people err in the direction of their own beliefs, the average should be
greater than zero. A simple, one-group t-test can be calculated using a predicted difference
of zero for the null hypothesis.
Further Reading
Mullen, B., Atkins, J. L., Champion, D. S., Edwards, C., Hardy, D., Story, J. E., &
Vanderlok, M. (1985). The false consensus effect: A meta-analysis of 115 hypothesis tests.
Journal of Experimental Social Psychology, 21, 262-283.
Ross, L., Greene, D., & House, P. (1977). The false consensus phenomenon: An
attributional bias in self-perception and social perception processes. Journal of
Experimental Social Psychology, 13, 279-301.
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