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Statistics is dead - long live statistics + Is climate change too complex to predict?

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Statistics is Dead – Long Live Statistics
https://blogstats.wordpress.com/2016/06/...tatistics/

EXCERPT: Lee Baker wrote an article that will please the whole community of official statistics where specialists of many thematic fields (and not alone statisticians or mathematicians or … data scientists) are collecting, analysing, interpreting, explaining and publishing data. It’s this core message that counts:

"...if you want to be an expert Data Scientist in Business, Medicine or Engineering” (or vice versa: An expert statistician in a field of official statistics like demography, economy, etc.) “then the biggest skill you’ll need will be in Business, Medicine or Engineering…. In other words, …. you really do need to be an expert in your field as well as having some of the other listed skills”

Here is his chain of arguments...



Is Climate Too Complex to Model or Predict?
http://www.popsci.com/is-climate-too-com...lt&src=syn

EXCERPT: A common incarnation of this skeptic argument says, “Even with state-of-the-art computer models, scientists can’t even accurately predict the weather two weeks from now, so how can they possibly tell us what’s going to happen twenty years from now?” Here we have one of those classic climate-skeptic mistakes. This mistake is confusing weather with climate. Weather is what you get day to day, which is something that can vary considerably based on all sorts of influences. It’s chaotic, in the scientific sense of the word. Chaos theory looks at the behavior of dynamical systems that are highly sensitive to initial conditions, and people have applied it to everything from data encryption systems to models that try to predict epileptic seizures. It is because of the extreme sensitivity to initial conditions that weather predictions are such a challenge. Even a tiny change in these initial conditions can have a dramatic effect on how things unfold, something known as the “butterfly effect.” (A marvelous 1952 science fiction short story by Ray Bradbury called “A Sound of Thunder” is based on this effect.)

Climate isn’t the same as weather. Climate is the average of weather over a long time — years or even decades. Think of it in terms of a slot machine at a casino. Predicting weather would be like trying to predict whether you’re going to win on the next pull of the lever, which, despite some folks’ belief that they can do this, is not going to be successful very often. Predicting climate, on the other hand, would be like predicting whether you’ll win or lose money averaged out over thousands of pulls of the lever. As casino balance sheets will tell you, the odds are pretty darn high that you’re gonna lose. Bringing it back to weather and climate: Consider when the temperature one day to the next differs by, say, five degrees Celsius (nine degrees Fahrenheit). You might not even notice such a change. It is an entirely different story if the climate changes by those same five degrees Celsius, because a climate change means that the average global temperature has changed. We’re no longer looking at a brief, local fluctuation.

[...] It’s important to keep in mind that we’re talking about climate here, not weather. The object is not to predict what temperature it will be in Delhi on, say, March 5, 2023, or exactly when the next hurricane will hit New Orleans. Instead, we want to predict the trend in the climate over the coming decades. Like modeling weather, this, too, is no easy task. Climate models have to account for interactions between the land surface, ice, oceans, the sun, and the atmosphere, among other factors. What’s remarkable is that despite their complexity, climate models do an impressively good job, as we’ll explain, and are improving all the time....
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