How are key data terms defined? How are data collected and reported? What theories guided the design of the models that process the raw data? What studies validated the models? How sensitive are the models to variations in inputs? How well do the models perform using historical data? Do the models have a track record at prediction — and if so, how well have they done? What alternative hypotheses were considered? How were the hypotheses tested?
Anyone surprised by such questions can’t plausibly claim to understand the science, much less to follow it. Most likely, they’ve confused “the science” with a selected scientist, a claimed scientific consensus, or the scientific establishment. Or, worse, partisan politics masquerading as science.
The confusion stems from a common misconception — an improper line many people draw between scientists working for corporations and scientists working for universities or government agencies. While most people understand that corporate scientists tend to support positions that serve corporate interests, many have been fooled into believing that academic and government scientists serve objective scientific truth.
Employment incentives are important to all scientists. The only difference is that it’s easier for outsiders to guess what a corporation wants its scientists to say than it is to understand what drives career advancement in academia or government.
With the absence of a bottom line or market feedback, success in academic or government science often flows to those most adept at flattering their more senior colleagues…
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