One of the great things about science is that we can determine whether a phenomenon is likely real without having to have the slightest clue what the mechanism might be. Evidence comes first, and this looks like solid evidence, and mechanism can come later; the evidence alone proves the existence of something interesting, and that's what's worth a follow-up.
Hypothesis generation is important, because it helps us design the next experiment, but this experiment is already very interesting.
> One of the great things about science is that we can determine whether a phenomenon is likely real without having to have the slightest clue what the mechanism might be.
You just have to ignore the people who shout "correlation is not causation" at every opportunity, appropriate or not.
Because "correlation is not causation" is just plain wrong. What is should be is "correlation does not imply causation". This is where science comes in. To answer the question, is this correlation because of causation?
The statement is only important to people doing statistical analysis not experimental science.
You say this implies causation... I have my doubts in any sense of the word implies.
For sure, a correlation could lead to something to investigate, but look at enough data and you will find plenty of correlations that mean nothing. A lot depends on how the correlation is discovered (number of variables involved etc.).
>You say this implies causation... I have my doubts in any sense of the word implies.
Couples divorcing people their partner got fat on margarine?
Besides that's not the best way to check correlation charts. You first have to remove bias components influencing both curves, e.g. the mere act that both are rising over time.
When you do that, do they still match each other, e.g. following increases and decreases? I very much doubt so. So this plot doesn't actually show correlation -- just that both "increase" over time in a similar way.
The same kind of "same plot trends" happens or every set of things that e.g. both have an exponential growth curve -- but it's not correlation unless both change consistently as the other changes.
That can't be right. There are so many spurious correlations that obviously imply nothing.
There's got to be some other required factor before correlation can imply causation. Like "if there's reason to believe something is relevant, and there is correlation, then that implies causation. "
Not disagreeing, but there is a fine-line between an argument being wrong and that the conclusion being wrong. Are you really right when you use an incorrect argument/process/information to arrive to the right conclusion? There's a whole epistemological debate to be had about the intersection of belief, knowledge and truth.
Hypothesis generation is important, because it helps us design the next experiment, but this experiment is already very interesting.