In any control system that is functioning properly, the methods used to control a signal won’t be correlated with the signal they’re controlling.
Worse, there will be several variables that DO show relationships, and may give the wrong impression. You’re looking at variables A, B, C, and D. You see that when A goes up, so does B. When A goes down, C goes up. D never changes and isn’t related to anything else — must not be important, certainly not related to the rest of the system. But of course, A is the angle of the road, B is the gas pedal, C is the brake pedal, and D is the speed of the car.
In the past, GDP and resources use have always been tightly correlated. But this is just drawing a line through some data — it’s not based on any deep theory. And in fact, these correlations can change very quickly. Just as one example, here’s energy use versus GDP since 1949.
If you were sitting in 1970, you could look at this curve and claim, very confidently, that economic growth requires concomitant increases in energy use. And you’d be wrong. Because the trend is your friend til the bend at the end.
There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance.