That’s a 0.3% consensus, not 97% by Joanne Nova
We’ve already found enough flaws, but Christopher Monckton analyzes John Cook’s 97% consensus paper and sharpens the scythe. He finds:
- It should never have been done, it’s an unscientific method — “consensus”
- The “consensus” was defined in three different ways. (Which hypothesis are they testing?) None of the three definitions is specific enough to be falsifiable.
- The paper strangely omitted the key results. (Why make 7 classifications, if they were not going to disclose how many papers fell into each category?)
- Of nearly 12,000 abstracts analyzed, there were only 64 papers in category 1 (which explicitly endorsed man-made global warming). Of those only 41 (0.3%) actually endorsed the quantitative hypothesis as defined by Cook in the introduction. A third of the 64 papers did not belong.
- None of the categories endorsed “catastrophic” warming — a warming severe enough to warrant action — though this was assumed in the introduction, discussion and publicity material.
- The consensus (such as there is, and it being irrelevant) appears to be declining.
The nice thing about this commentary is that Monckton provides a summary of the philosophy of science (showing Cook et al are 2,300 years out of date). Monckton has also checked Cook’s own data which was finally provided (several weeks after publication) and compares Cook to Oreskes, Anderegg, and Doran and Zimmerman and explains why they are wrong too.
Previously I’ve also pointed out the 12 reasons the paper fails, including that the number of papers is merely a proxy for funding, not evidence about the climate; most of the papers merely assume man-made warming is real, and some papers are 20 years old and the evidence has changed.
The non-disclosure in Cook et al. of the number of abstracts supporting each specified level of endorsement had the effect of not making available the fact that only 41 papers – 0.3% of all 11,944 abstracts or 1.0% of the 4014 expressing an opinion, and not 97.1% – had been found to endorse the quantitative hypothesis, stated in the introduction to Cook et al. and akin to similar definitions in the literature, that “human activity is very likely causing most of the current GW (anthropogenic global warming, or AGW)”.
The real risks of cherry picking scientific data by Matt Ridley and his blog The Rational Optimist
The Tamiflu tale is that some years ago the pharmaceutical company Roche produced evidence that persuaded the World Health Organisation that Tamiflu was effective against flu, and governments such as ours began stockpiling the drug in readiness for a pandemic. But then a Japanese scientist pointed out that most of the clinical trials on the drug had not been published. It appears that the unpublished ones generally showed less impressive results than the published ones.
Imbued as we are with an instinctive tendency to read meaning into nature, we find it counter-intuitive that many experiments get significant results by chance and that the way to check if this has happened is to repeat the experiment and publish the result. When the drug company Amgen tried to replicate 53 key studies of cancer, they got the same result in just six cases. All too often scientists publish chance results, or “false positives”, like gamblers or fund managers who tell you about winners they backed.
Outside medicine, we popular science authors are probably guilty of too often finding startling results in the scientific literature and drawing lessons from them without waiting for them to be replicated. Or as Christopher Chabris, of Union College in Schenectady, New York, harshly put it about the pop-psychology author Malcolm Gladwell: cherry-picking studies to back his just-so stories. Dr Chabris points out that a key 2007 experiment cited by Gladwell in his latest book, which found that people did better on a problem if it was written in hard-to-read script, had been later repeated in a much larger sample of students with negative results.
We seem unsettled by not knowing. We are also today drowning in data that will support conclusions that can prove to be very deceptive. This requires us to be more skeptical, not less. If we do not try to duplicate research results aggressively we risk drawing a lot of wrong conclusions. This is also easily abused by interested parties who can prey on the statistical ignorance of even our most educated leaders. When these conclusions support political objectives, hostility greets skepticism and verification.
Google and our mobile devices gives us all the answers and endless information at the touch of a finger. The questions and the wisdom to understand what we read is still up to us. Without this most human part of intelligence all we have done is speed up ignorance.
Do people mind more about inequality than poverty? from Matt Ridley at his blog The Rational Optimist
If you measure consumption inequality, it is far lower than pre-tax income inequality, because the top 40 per cent of earners pay more in than they get out, while the bottom 60 per cent get more out than they pay in. Indeed, in Britain the top 1 per cent generate about 30 per cent of the total income-tax haul. After such redistribution, the richest fifth of the population has only four times as much money to play with as the poorest fifth.
With big increases in housing benefit and other redistributions, consumption inequality may be as low as it has ever been. Add in the value of pensions (including the state pension), free healthcare, the fall in the price of food and clothing relative to wages, plus the dramatic fall in the cost of much technology and it is clear that for most basic needs, the country has never been less poor or less unequal. A smartphone’s search engine may be about as capable as a plutocrat’s full-time secretary was in 1960.
Imagine being told that one of the people in a meeting is a genuine billionaire (I owe this idea to Professor Don Boudreaux). How would you tell which one? His bodyguards, private jets and grouse moors are outside the room; his shirt and jeans are unlikely to give him away (as they would in 1900); his Rolex could be a cheap imitation; his teeth, girth and height are probably unremarkable (unlike in 1800); even his Diet Coke is the same as everybody else’s. Much more than in the past, most inequality in this country these days — though by no means all — is in luxuries, rather than necessities.
Measurements of inequality are not always indicative of the truly experienced equality. Making the rich poorer does not inevitably make the poor richer.Income is only one measure of the wealth of any class. The quality of consumption is missed by much of the data.
Inequality occupies the minds of the elite and the academics. Most people at the low end of the scale just wants to improve their lot in life. That is more important than how well others are doing. Other than the new political elite who get massive checks from their crony relationships with the government, few people who depend on government subsistence will ever get enough of it to escape poverty.
From Daniel Greenfield’s excellent blog, Sultan Knish, The Inequality of Access:
A thief is still a thief whether he wears a mask, a suit or a t-shirt with a social justice slogan. When people appoint thieves to steal for them, they shouldn’t be surprised when the thieves also steal from them. As the scorpion said to the frog, “You knew what I was when you let me ride.”
The voters who most depend on government vote to break it far more thoroughly than any Tea Party politicians could. No Republicans could have done to Detroit what Detroit did to Detroit. Not even the most extreme Tea Party politician could have done as much damage to the Federal government as Obama did.