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as having an important effect on their perceptions of global warming? Recent Pew Research Center polls have shown fairly significant short-term shifts in the number of Americans who believe there is evidence of global warming. Between June 2006 and January 2007, there was a 7% increase (70% to 77%) among U.S. residents who indicated there was “solid evidence” that the Earth is warming. However, between January 2007 and April 2008, the percentage decreased by 6% (77% to 71%). This decline in public acceptance of the evidence of global warming may be an aberration in a long-term trend of increasing belief. However, the shift does raise questions regarding the underlying factors affecting public acknowledgement of global warming. What types of evidence are individuals reacting to? [The writers note a significant short-term shift in views of Americans on global warming but then go on to frame a new research question about the kinds of evidence upon which individuals are basing their claims. This will in turn lead to further opinion polling and statistical analysis.]

Interpreting the Numbers: A Psychology Professor Speaks

In the following Voice from Across the Curriculum, psychology professor Laura Edelman offers advice on how to read statistically. She expresses respect for the value of numbers as evidence, as opposed to relying on one’s own experience or merely speculating. But she also advises students to be aware of the various problems of interpretation that statistical evidence can invite.

Voices from Across the Curriculum

The most important advice we off er our psychology students about statistical evidence is to look at it critically. We teach them that it is easy to misrepresent statistics and that you really need to evaluate the evidence provided. Students need to learn to think about what the numbers actually mean. Where did the numbers come from? What are the implications of the numbers?

In my statistics course, I emphasize that it is not enough just to get the “correct” answer mathematically. Students need to be able to interpret the numbers and the implications of the numbers. For example, if students are rating satisfaction with the textbook on a scale of one (not at all satisfied) to seven (highly satisfied) and we get a class average of 2.38, it is not enough to report that number. You must interpret the number (the class was generally not satisfied) and again explain the implications (time to choose a new text).

Students need to look at the actual numbers. Let’s say I do an experiment using two diff erent stat texts. Text A costs $67 and text B costs $32. I give one class text A and one class text B, and at the end of the semester I find that the class using text A did statistically significantly better than the class using text B. Most students at this point would want to switch to the more expensive text A. However, I can show them an example where the class using text A had an average test grade of 87 and the class with text B had an average test grade of 85 (which can be a statistically significant diff erence): students see the point that even though it is a statistical diff erence, practically speaking it is not worth double the money to improve the class average by only two points.

There is so much written about the advantages and limitations of empirical information that I hardly know where to begin. Briefly, if it is empirical, there is no guesswork or opinion (Skinner said “the organism is always right”—that is, the data are always right). The limitations are that the collection and/or interpretation can be fraught with biases and error. For example, if I want to know if women still feel that there is gender discrimination in the workplace, I do not have to guess or intuit this (my own experiences are highly likely to bias my guesses): I can do a survey. The survey should tell me what women think (whether I like the answer or not). The limitations occur in how I conduct the survey and how I interpret the results. You might remember the controversy over the Hite Report on sexual