I am linking to a blog post on statistical significance that may help explain why I am such a skeptic on the whole “human caused warming” claim:
Here’s an excerpt:
“Which is to say that according to my real, genuine, mathematically legitimate, scientifically fabricated scientific statistical scientific model (calculated on a computer), I was able to produce statistical significance and reject the “null” hypothesis of no cooling. Therefore there has been cooling. And since cooling is the opposite of warming, there is no more global warming. Quod ipso facto. Or something.
I was led to this result because many (many) readers alerted me to a fellow named Lord Donoughue, who asked Parliament a question which produced the answer that “the temperature rise since about 1880 is statistically significant.” Is this right?
Not according to my model. So who’s model, the Met Office’s or mine, is right?
Well, that’s the beauty of statistics. Neither model has to be right; plus, anybody can create their own.”
Read on: http://wmbriggs.com/blog/?p=8061
The post explains very clearly, at least to me, why statistical significance may not be significant at all. Another important excerpt:
“His model, which is frankly absurd, is to say the change in global temperatures is a straight linear combination of the change in “anthropogenic contributions” to temperature plus the change in “natural variability” of temperature plus the change in “measurement error” of temperature. (Hilariously, he claims measurement error is of the order +/- 0.03 degrees Celsius; yes, three-hundredths of a degree: I despair, I despair.)” (Bold is mine)
Proxies cannot possibly yield the accuracy required for the claims made by climate science concerning warming. They simply lack the degree of accuracy needed. We really can’t even measure the accuracy except in very modern times. Perhaps if we gave the proxy to 25 unrelated scientists and had them all give their interpretation of the proxy, it would become apparent that this is not an accurate measurement. If you lack accurate measurements, then your conclusion cannot be said to be accurate based on those measurements. There are too many assumptions and too much use of “estimated” and proxy data to give any kind of accurate result, except by random chance. We simply do not have the data for these types of claims.