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The author points out that we all know that "correlation does not indicate causation", but there is an equally important error often drawn from data: The confusion of statistical significance with real world meaning.
As science grows more powerful and government more technocratic, the stakes of correlation—of counterfeit relationships and bogus findings—grow ever larger. The false positive is now more onerous than it's ever been. And all we have to fight it is a catchphrase.
Statistics are often used incorrectly to imply causation.
As an expert on radiation (Diplomat of the American Board of Radiology in Therapeutic Radiation Physics), I have seen this over and over.
For example, if you look at the frequency of 300 illnesses (I've seen this done in research papers) versus exposure to anything (for example, microwaves), at the 95% confidence level you would expect 5% of the illnesses (15 in this example) to randomly show a correlation to the exposure.
Despite this statistical fact, if out of the 300 illnesses, 4 conditions appear to be related to exposure to microwaves (at the 95% confidence level), the researchers will state that this "proves" that the 4 conditions are "caused" by microwave exposure, despite the fact that there is not even a postulated physical mechanism for microwaves to cause the "observed" effects.