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For better or for worse, science has long been married to mathematics. Generally it has been for the better. Especially since the days of Galileo and Newton, math has nurtured science. Rigorous mathematical methods have secured science’s fidelity to fact and conferred a timeless reliability to its findings.
During the past century, though, a mutant form of math has deflected science’s heart from the modes of calculation that had long served so faithfully. Science was seduced by statistics, the math rooted in the same principles that guarantee profits for Las Vegas casinos. Supposedly, the proper use of statistics makes relying on scientific results a safe bet. But in practice, widespread misuse of statistical methods makes science more like a crapshoot.
Originally posted by Xcalibur254
Here is a new article that can act as a compliment to the thread on falsification of data. I'm not quite sure how to feel about this. My personal field of research is in psychology, so I use a lot of statistics and I thought I had a pretty good grasp on the concepts.
We always try to provide 95% confidence intervals for results.
Originally posted by zvezdar
The misuse of statistics is prevalent throughout society, and not just in the sciences (look at how the misuse of statistical models has afflicted finance...despite the many failings complex models are still being used as a source of truth when they are nothing of the sort). However the fact that many scientists have little statistical training means that the statistics and statistical models used in science seem particularly weak and prone to misuse.
Medical science is an obvious one but climate science is another where the statistical training simply isnt there to properly understand the shortcomings of what they are doing. They put faith in models which are not suited to what they are doing. The simple fact that the ability to hindcast does not demonstrate predictive power doesnt even seem well understood by most!
Physics has been afflicted with a slight variation of this issue, where the mathematical abstractions have become so complex that they have veered physicists away from reality and into the imagination.
Originally posted by Astyanax
reply to post by Xcalibur254
I have often thought this myself, and usually about medicine.
I think the eraly success of statistical analysis in correctly identifying the association between smoking and lung cancer, even when the mechanism by which smoking causes cancer was not known, sparked a slow-building explosion in the field of statistical medicine. Nowadays, it seems that the majority of popular news items on health, diet, etc., are based on statistical studies or metastudies (studies of studies). That's how we learn that drinking so many millilitres of red wine a day makes people less prone to heart attacks, that people who exercise live longer, and so on. For all we really know, none of it is true.
I tend not to believe what I read in the media about medical subjects for precisely this reason--most of what is presented as near-certain is based merely on some statistical correlation, without any causal mechanism established to back it up.
This over-reliance on statistics is not an indictment of science in general, by the way--merely of those who place too much confidence in dubious statistical analysis.
Originally posted by mbkennel
Climatology is not dependent on statistics, but on physics. That the ability to hindcast doesn't demonstrate predictive power ---by itself --- is perfectly understood by climatology. These models are not at all just data-driven statistical models, but physical models based on specific individual mechanisms consistent with the laws of chemistry and physics. And in this sense, the combination of the ability to hindcast plus evaluation of the fidelity and sensibility of the equations of motion give more confidence. Climatology is 95% physics and 5% statistics.
True, but this can still be the right thing to do. Otherwise everybody would still be smoking and dying until every possible mechanism is elucidated.
How much is 'over-reliance' and how much is proper reliance (on statistics)?
Originally posted by zvezdar
Originally posted by mbkennel
Climatology is not dependent on statistics, but on physics. That the ability to hindcast doesn't demonstrate predictive power ---by itself --- is perfectly understood by climatology. These models are not at all just data-driven statistical models, but physical models based on specific individual mechanisms consistent with the laws of chemistry and physics. And in this sense, the combination of the ability to hindcast plus evaluation of the fidelity and sensibility of the equations of motion give more confidence. Climatology is 95% physics and 5% statistics.
I disagree, the only way that climate scientists have been able to make a tenuous case for disasterous climate change is by using statistical models to recontruct past climate via proxies. The idea being that to demostrate that current climate change is due to a different driver from the past requires a statistically significant deviation from past climate history. Without that all you have is a dynamic climate and no disasterous 'change'.
That is where the whole 'climate change' paradigm breaks down, the statistical models they have been using are completely inadequate for what they are trying to achieve and time after time it has been shown that the limitations of their models are poorly or incompletely understood. The only reason they pass the peer review system is because everyone else that is involved has an equally poor understanding of statistics.
The current radiative physics models are completely incapable of longer-term climate prediction (and the dynamic physics of the earth's climate remains poorly understood overall), hence a reliance on arguments based on the statistical reconstructions. Take away the reconstructions and all you have is short-term predictive power and no 'catastrophe' on the cards.
For me short-term climate modelling is 95% physics, but the 'climate change' issue iself is about 5% physics and 95% everything else (included in that 95% is poor statistics).
Another post above mentions confidence intervals, the use of 95% confidence intervals seems to come from the way statistics is taught in universities; the 95% level is always used to test a one-tailed null hypothesis. Its usually not questioned by students and one of those things that is accepted. However after seeing financial models built to 99.5% confidence fail during the finance crisis it makes the use of a 95% CI even more ridiculous as a benchmark for significance in important trials.