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Teaching uncertainty to intronon-science majors  

One of the things I find most difficult about teaching climate change science to intro and/or non-science majors is the issue of uncertainty in communicating science - particularly in regard to climate change. For example, the IPCC states that it is 'very likely' human activity has lead to current observed changes. But, in discussing this with intro level students, there are some who will disregard any claims that are not 'positively certain' - Of course, this stems from misconceptions about the scientific process, which, in a large lecture hall, can be difficult to share with students.

On the topic of hurricanes and climate change, there are large uncertainties. We have hypotheses regarding the connection, but, at this point, little data. This make for a very interesting teaching topic precisely because of this...But what is the best way to introduce students to this uncertainty?

Looking forward to hearing your thoughts/ideas on this!


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I think it's useful to contrast the relatively high degree of certainty about substantial human influence on global mean temperature with the highly uncertain state of affairs regarding human impacts on Atlantic hurricanes. This drives home the message that not all aspects of anthropogenic climate change have similar degrees of uncertainty, according to scientific expert opinion, such as is contained in the IPCC. So you can't paint every part of this problem with the same brush...For those who require positive certainty before accepting something, you might try to think of other analogies from the real world.

Take the case of life insurance as an example. You wouldn't want to wait until you were absolutely certain you were going to die in the next week before purchasing life insurance. By requiring such a high degree of certainty about an outcome, you are essentially tying your hands and ensuring that you will probably not be insured when you die (who will insure you when your fate becomes obvious?) And such a requirement of certainty before taking action (buying insurance) will be to the detriment of anyone depending on you for financial support. There are many other examples one can use here.


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Clearly teaching about uncertainty is introducing students to probability. As college students, they should have been exposed ot the concepts of probability but they often have not and even if students understand the probability of getting a certain outcome with a dice, a probability of 30% that it will rain is much more difficult to comprehend. Do we even understand the scientific concept that if we go 10 times out it will rain 3 times? To me the application of probability concepts is more than a scientific concept becuase its value resides in the fact that it entails making decision with this knowledge. There is a lot of material on the science of risk but there again it is mostly mathematical. Personally I consider that the application of uncertainty (with regard to climate change or other topic) to once life relates to cricital thinking ability, to the abikity to make decision with incomplete knowledge. And it is both an issue of knowledge and of once ability to handle uncertain outcome. So I agree iwht you that it is difficult to teach but we have many examples in daily life where people face uncertainty and deal with it. So if you use some of these daily examples (and the present financial world provides us with many), your students might be better able to better grasp the concept and apply it to climate change or hurricane.


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I've approached this problem as part of a second issue on how much trust can be placed in scientific data, results, etc. So I end up tackling the uncertainty as part of an in-depth look at the scientific method, teaching the students exactly where the checks should be and also where some of the cracks and loop-holes are (such as with the Korean cloning claim incident). Many of my students are taught to disbelieve climate claims because of the statistical weaknesses in paleo data. Rather than fight statistics with statistics, I try to show where the methodologies differ in their reliance on statistical data, and how the results are improved using multiple working hypotheses. In other words, there is more than one way to look at uncertainty, number crunching is just one solution.


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