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Recently, the U.K. Met Office announced a revision to the Hadley Center historical analysis of sea surface temperatures (SST), suggesting that the oceans have warmed about 0.1 degree Celsius more than previously thought. The need for revision arises from the long-recognized problem that in the past sea surface temperatures were measured using a variety of error-prone methods such as using open buckets, lamb's wool–wrapped thermometers, and canvas bags. It was not until the 1990s that oceanographers developed a network of consistent and reliable measurement buoys.
[...] But that's where the good news ends. Because the oceans cover three fifths of the globe, this correction implies that previous estimates of overall global warming have been too low. Moreover it was reported recently that in the one place where it was carefully measured, the underwater melting that is driving disintegration of ice sheets and glaciers is occurring far faster than predicted by theory—as much as two orders of magnitude faster—throwing current model projections of sea level rise further in doubt.
(Score: 2, Interesting) by Anonymous Coward on Wednesday August 21 2019, @06:33AM (9 children)
I have seen this effect in my area (medical research - yeah, fuck you p-haters).
Bunch of studies reporting nothing special then one day someone discovers a physical mechanism that suggests there should be an effect. Now everyone reports seeing this effect in their data. It's sad but "real" scientists are actually a conservative bunch, shit-scared of standing out from the crowd. Much easier to hide in the nonsignificant space.
(Score: 0) by Anonymous Coward on Wednesday August 21 2019, @08:41AM (8 children)
That should tell you how worthless statistical significance is... Even doctors know to ignore it unless it supports their preexisting belief. And how hard is it to come up with some mechanism to explain any result? Just add more feedbacks and pump and receptors, etc till it makes sense.
(Score: 0) by Anonymous Coward on Wednesday August 21 2019, @11:52AM (2 children)
And I should say, when I did medical research that was exactly the state of the field. Then when I calculated how much room it would take to hold all these supposed components and energy required to run it, it was almost the entire energy and space budget of the cell. All that for a single relatively minor signalling system, just one among thousands or tens of thousands of things the cell needs.
Medical researchers are amazingly quantitatively ignorant, which leads them down some very silly paths.
(Score: 2) by FatPhil on Wednesday August 21 2019, @02:45PM (1 child)
Great minds discuss ideas; average minds discuss events; small minds discuss people; the smallest discuss themselves
(Score: 0) by Anonymous Coward on Wednesday August 21 2019, @05:41PM
The latter get paid waaaaay better.
(Score: 0) by Anonymous Coward on Wednesday August 21 2019, @12:36PM (4 children)
Doctors are not scientists. Most don't know statistics.
https://www.bbc.com/news/magazine-28166019 [bbc.com]
(Score: 0) by Anonymous Coward on Wednesday August 21 2019, @01:18PM (3 children)
Almost no one researching anything in medicine has any understanding of statistics. If you see "p < 0.05" or the phrase "statistically significant", that is a shibboleth. It shows you are in the group of people who have no idea what they are doing and can be trusted not to be too harsh.. since your closet is filled with crappy unreliable conclusions too.
(Score: 1, Funny) by Anonymous Coward on Wednesday August 21 2019, @05:54PM (2 children)
Exactly I prefer my hypotheses to be taken as a priori true from from a biblical source. WMD in Iraq? Let's ask the Decision Maker. Vaccines cause autism? Teach the controversy. Trickle down tax cuts? True true true. No p values necessary.
(Score: 0) by Anonymous Coward on Wednesday August 21 2019, @06:19PM (1 child)
I prefer science. NHST is no better than a bunch of monks praying... whether you use pseudoscience or dogma doesn't make much difference to me.
(Score: 2) by Azuma Hazuki on Thursday August 22 2019, @12:02AM
I've only ever seen you shit on the null hypothesis paradigm, but so far haven't seen you offer an alternative (some sort of Bayesian paradigm maybe?)
What do you have in mind as its replacement, and how do you know that it will perform better? Unless you have something better in mind, all you're doing is blowing raspberries at the best tool by a large margin we have. Put up or shut up.
I am "that girl" your mother warned you about...