I agree. I often see statistics used in political and social debates but in reality statistics (in these areas) mean very little. IMO, statistics are the least accurate form of data collection. They depend on accuracy and honesty of reporting, taking into account ALL possible factors (which they never do) and there is never a control situation. They are not double blind studies and even those are blighted with interpretation bias.
And depending on who collected the stats, what the source of reporting was or where the source of reports was, you may get entirely different data which shows that you will get conflicting data all over the place. Which should rule out a positive correlation and proof of causation. But people who use these as argument points do so because they have nothing else to go on other than their own personal beliefs and/or fears, especially in political debates. They want their fears or beliefs to be justified by science so they through numbers out there, number that seem to support their point of view, but the other side has the same arsenal of numbers and both sides are able to interpret the numbers to their own whims. Both will claim the other person's numbers are wrong or lies when it may be that they are both being technically honest. but they both fail to realize that the statistics simply do not support either side of the debate.
The problem is, when statistics are used in a political debate they are asking you to trust both:
1) - The raw data
2) - The presenter's interpretation of that data.
#2 is always suspect in a political debate.
Statistics are easy to distort because the required brevity of a statement made in political debate requires most of the raw information to be omitted. You're only hearing conclusions, with a smattering of the data behind them, and are being asked to assume the rest of the data exists without knowing whether it exists or not.
that's fine if the correlation really implies causation and we understand how causation forces the 2 trends to be correlated - if not then we end up with a rule of thumb, and the trouble with rules of thumb is that they only apply within a limited field of experience and break down outside it
Mm, and what complicates matters is the shear complexity of Economics. There are so many factors to consider that even if you do have a knowledge of direct causation between two factors, there are so many interacting variables that making accurate economic predictions becomes overwhelmingly difficult.
Yeah. The problem is that the system is dynamic. There are chain reactions where one thing causes another, which causes another, ... etc.
In he health insurance industry, on the other hand, they system an insurance person analyzes is less dynamic. They can use statistical analysis to determine who will or will not fall off a roof to a higher degree of reliability, because one person falling off a roof usually doesn't cause another person to fall off a roof also. They're isolated events. If anything causes them it's an external factor, and so that factor may be detectable because it is clearly isolated from its effects.
But when effects are causes, and causes are effects, that's just a mess.
hence it's no good showing that a correlation has held true for the last 100 years to imply that it always will : you first need to understand what is the basis of the correlation in order to determine under what circumstances it will hold true in the future
I am reminded here of the black swan theory - just because all of the swans you have ever seen have been white, does not mean to say that all swans are white, or that we should not consider the possibility of observing swans of a different colour in the future. I guess this is basically about staying awake and not becoming too complacent in our assumptions and paradigms.
That's a problem of using large scale observations to make small scale predictions. However you can still predict that the next swan you see has X% probability of being white. You just can't say for sure it will be white.
You can also predict that out of a large group of swans, most of them will be white.
However, just like insurance, you have the advantage here that the cause of swans' being one color or another is (mostly) an external cause.