Guest post: How inferential science works and why it matters

Guest post by James Garnett.

How inferential science works, episode one: the null hypothesis.

Ever wonder what things like medical studies are actually showing, and why they are sometimes (often?) disproved?

Inferential studies attempt to demonstrate a correlation between two things, generally speaking. That correlation is stated in a way that can be tested, through what is called a null hypothesis. Think of it as the default assumption. For example, in simple (aka not rigorous) terms: “the amount of cholesterol in the food that a person consumes is correlated to the amount of cholesterol present in their blood”. A statement of that nature can be tested, and disproved.

But null hypotheses cannot be proved. There are simply too many factors at play. It’s sort of like jury trials in the USA: we don’t prove someone innocent, we prove them “not guilty”. You can never prove innocence, you can only show that you don’t have evidence to prove guilt.

Moreover, a proper null hypothesis can be very hard to formulate. The example that I used above is a bad one, for example, because there are different types of cholesterol (among other reasons).

So a lot of studies start out with improper null hypotheses, and review plus later studies show their results to be unreliable. (Plus there is this problem of pressure to show positive results, rather than negative ones. Nobody cares if a scientist shows that bird feathers don’t cause prostate cancer, to indulge in a bit of hyperbole. But negative results matter.)

This is why the idea that “GMO’s are bad” is a faulty starting point. Which GMO’s? What methods of engineering? You have to be specific. If you’re not being specific, you’re reacting upon emotion and intuition—that is not science. It may be a starting point for science, but it’s not conclusive or reasonable in and of itself. You may believe that corn seed variety X that is GMO is bad, but what does that say about the oil from GMO olives? Nothing. Specificity matters.

This is one of the major reasons why GMO labeling is a bad idea, and why it keeps losing when brought up during popular election initiatives. The labels don’t tell you anything of value—they only play upon your fears.

And yes, I’ve been called a “shill for Monsanto” for stating this kind of opinion in the past! Still waiting for my first Shill Royalty Check, though.

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