Professors at a leading Canadian university declare the Hawthorne effect is "a mess of sloppiness and misogyny".
Professor states it's because of the math required.
"Scientists finally acknowledge that they got their solar cycle predictions wrong, and that we are fast approaching the sun's explosive peak"
Another "science is settled" bites the dust.
Bryan Caplan asks--and answers--an excellent question: Why can’t supporters of paternalism be honest?
Seems pretty important to me.
[T]he results imply that the effect of man-made CO2 emissions does not appear to be sufficiently strong to cause systematic changes in the pattern of the temperature fluctuations. In other words, our analysis indicates that with the current level of knowledge, it seems impossible to determine how much of the temperature increase is due to emissions of CO2.
Maybe. But even if true in my estimation the cost outweighs the benefit.
". . . none of the answers we have come up with are satisfying."
On the one hand there is a staggeringly large amount of planets in the universe so one would think we can't be the only intelligent life. But on the other hand there is a vast number of things that had to go right for us to emerge--a giant meteor impact and/or volcano eruptions that killed the dinosaurs for one example--that maybe we are truly alone.
"Following a failure to replicate, “Super Size Me” paper turns out to be full of numerical inconsistencies"
I quibble with the piece's "first clue". My first clue is that it was, even before the "test," a dopey hypothesis.
More on retractions:
Gary N. Smith, Fletcher Jones Professor of Economics, Pomona College, addresses something I've wondered about but haven't yet taken the time to try to figure out: how do these machine learning algorithms establish causality?
Artificial intelligence (AI) algorithms are terrific at discovering statistical correlations but terrible at distinguishing between correlation and causation. A computer algorithm might find a correlation between how often a person has been in an automobile accident and the words they post on Facebook, being a good software engineer and visiting certain websites, and making loan payments on time and keeping one’s phone fully charged. However, computer algorithms do not know what any of these things are and consequently have no way of determining whether these are causal relationships (and therefore useful predictors) or fleeting coincidences (that are useless predictors).
And here he proposes the "Smith Test" to determine if computer algorithms can make reliable recommendations.