By Dan Lainer-Vos
Adjunct Assistant Professor of Sociology, University of Southern California
Have you had the experience of discussing climate change only to be interrupted by a wise chuckle from a person who suggests that our planet has known natural fluctuations in the past and that, therefore, it is possible that the spate of record-breaking temperatures of past decades reflects naturally occurring fluctuation?
The climate-change denier, in such instance, presents him or herself as a hard-nosed skeptic while suggesting that the climate researcher community is hysterical. To an extent, this interaction is the story of climate change debate over the last twenty years—a long drawn out argument that is fed by the very fact that science, including climate science, is built on probabilistic models where absolute certainty is simply not part of the game. Is there a way out this pickle? Thinking about statistical inference, and especially the types of errors that statisticians are concerned with, can shed new light on this debate.
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