This exercise helps students to become critical interpreters of evidence. They learn how to identify cognitive biases in arguments that they critique, and they learn how to watch out for biases in their own work. The assignment should be distributed
In the paper that started the work on cognitive biases,[1] Amos Tversky and Daniel Kahneman describe systematic mistakes about probability that even academics tend to make when using or interpreting evidence. The article in its entirety would overwhelm students, but the individual descriptions of biases are accessible, and each one is less than a page long. Assign each student one bias, and give him or her only that section of the paper. The students study the bias and fill out the worksheet below.
There are six biases that are most accessible and relevant for ENWR students.[2] Depending on the size of the class, there will be two to four students working on each bias. The day the individual reports are due, have students meet in groups according to bias. In each group, students compare notes and plan a way to teach the class about their bias. (You may have each group design a handout. You can collect the handouts and photocopy them to give out when the groups make their presentations.) The students remember well the bias that they teach. You might ask students to bring in examples of the bias that they notice throughout the semester in their academic or newspaper reading.
Vocabulary that will help you as you read about your assigned bias:
1. Explain the cognitive bias:
2. Give an example (or better, give two):
3. How can we identify this bias in others’ work?
4. How can we avoid this bias in our own work? [1] The Tversky and Kahneman article is published in [2] (1) insensitivity to prior probability of outcomes, (2) insensitivity to sample size, (3) misconceptions of chance, (4) biases due to the retrievability of instances, (5) biases of imaginability (Assign this one to students who enjoy math, or tell the students not to worry about the math because they can understand the gist of the bias with out the mathematical example given.), and (6) insufficient adjustment The paper is divided into subtitles by the names of the biases. |
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