Beam me to Planet Gore !
Statistics is strange....mathematics....usually.
Lies, damned lies and statistics...
If a large number of people have come to intuitively understand that after reading decades of hysterical “science” news stories, then this isn’t really that much of a revelation. There are a lot of fields of study that are treated as a science when they’re not.
Brings to memory Bayes Theorem.
Statistics is a well understood mathematic discipline. It’s weaknesses and strengths are well documented. The problem is not the math behind science, it is the persons that are intentionally trying to sell a falsehood.
There are entire books written on how to deal with alpha and beta errors which this article insinuates are not understood.
Easy way to frame this argument. If you were a high school science student and had to use statistical “smoothing” to produce the “right” data, what kind of grade would you get?
It seems to me that it is less over-reliance on statistics, than misapplication of statistics that is at fault in the state of “climate science”. And this misapplication has two aspects: the much-discussed one involving dishonesty—selecting weather stations about which statistical inferences must be drawn (due to gaps or the need to estimate an urban heat-island effect) in preference for ones with long continuous track records in rural areas, omitting weather stations in colder regions (e.g. the Andes) and making inferences based on “nearby” stations in areas with radically different climate—and one involving an honest conceptual error.
The conceptual error lies behind the “weather is not climate” mantra, that hides the fact that climate IS weather, averaged over longish-time intervals, but I think also hides the mistaken assumption that the variability of weather is random noise of the sort statistical methods are useful for dealing with. In fact, the unpredictability of weather is due to the underlying non-linear dynamics that does not go away when you take time averages.
The current trend is to model a dynamic system, and then calibrating the model to historic statistics by using hidden assumptions, (which can be really wrong.) Then the modelers feel confident in using the model to predict the future and to regulate the heck out of you.
Dear reader remember that you can’t PROVE anything with statistics.
It all comes down to integrity and honor.
This just in. The IPCC has declared there is a consensus that this article is wrong, within a 90% confidence factor . . . .