Paper published by Net Zero Watch (PDF).

Introduction:

A fascinating experiment was conducted not too long ago. An experiment about experiments. About how scientists came to conclusions in their own experiments. What happened was this: social scientist Nate Breznau and others handed out identical data to a large number of researchers and asked each group to answer the same question. The question was: Would immigration reduce or increase ‘public support for government provision of social policies’?

That can be difficult to remember, so let’s reframe this question in a way more memorable, and more widely applicable to our other examples. Does X affect Y? Does X, more immigration, affect Y, public support for certain policies?

That’s causal language, isn’t it? X affects Y? These are words about cause, about what causes what. Cause, and knowledge of cause, is of paramount importance in science. So much so that I claim – and I hope to defend the idea – that the goal of science is to
discover the cause of measurable things. We’ll get back to that later.

Just over 1200 models were handed in by researchers, all to answer whether X affected Y. I cannot stress enough that each researcher was given identical data and asked to solve the same question.

Breznau required each scientist to answer the question with a ‘No’, ‘Yes’, or ‘Cannot tell’. Only one group of researchers said they could not tell. Every other group produced a definite answer. About one quarter – a fraction we should all remember –answered ‘Yes’, that X affected Y – negatively. That is, more X, less Y.

Now researchers were also allowed to give some idea of the strength of the relationship, along with whether or not the relationship existed. And that one-quarter who said the relationship between X and Y was negative ranged anywhere from a strongly negative, to something weaker, but still ‘significant’. Significant. That
word we’ll also come back to.

You can see it coming…about another quarter of the models said ‘Yes’, X affects Y, but that the relation was positive! More X, more Y, not less! Again, the strength was anywhere from very strong to weak, but still ‘significant’.

The remaining half or so of the models couldn’t quite bring themselves to say ‘No’: they all still gave a tentative ‘Yes’, but said the relationship was not ‘significant’.

You see the problem. There is, in reality, only one right answer, and only one strength of association, if it exists. That a relationship does not exist may even be the right answer. I don’t know what the right answer is, but I do know only one can be. Yet the answers – the very confident, scientifically derived, expert-investigated answers –
were all over the place and in wild disagreement with each other.

Every one of the models was science. We are told we cannot deny science. We are commanded to Follow The Science.

But whose science?