Now I don't have a copy of the book. But I can backtrack a little bit. The second Guardian story also links to a large meta-analysis that the author published in 2017.
At this point my ability to reconstruct the 2045 date gets a bit shaky. We have these two figures, which even extrapolating linearly aren't going to get you to zero this century.
Ah, but this looks promising.
Here's the "meta-regression model for mean sperm concentration by fertility and geographic groups, adjusted for potential confounders."
Now we just have to extend the axes a little bit, extend the trend line....and BINGO!
We hit Sperm Zero in 2045.
When you see something like this, it's worth asking yourself: what's going to happen 2046?
So....we found something that seemed to bad to be true, traced it back to the source, and indeed, it's far too bad to be true—or at least to be backed by any credible evidence whatsoever.
We've got a linear regression line from some sort of meta-analysis, extrapolated out as far again as the underlying data are drawn from.
No underlying mechanistic model.
And a ridiculous conclusion of negative sperm in 2046.
Which would be a heartbreaker, because we'd never survive as a species long enough to run negative 100m dash times.
My colleague, epidemiologist @joel_c_miller, has done a great job of debunking mis- and disinformation throughout the pandemic. In this great thread, he takes on the claim that COVID is basically harmless, and any excess deaths are due to fear and stress from social precautions.
Instead of calling the person an idiot, he does nice job of explaining how you might test such a hypothesis — and then looks to the data to show that this story about fear and stress is entirely unsupported. The whole thing is well worth a read.
But there's something else interesting here.
The fear-and-stress argument is introduced with an historical account about a medieval experiment conducted by medieval Persian philosopher Avicenna / Ibn Sīnā.
I love seeing journalists do a textbook job of calling bullshit on the misleading use of quantitative data.
Here's a great example. @RonDeSantisFL claimed that despite having schools open, Florida is 34th / 50 states in pediatric covid cases per capita. nbcmiami.com/news/local/des…
I don't know for certain what set off their bullshit detector, but one rule we stress in our class is that if something seems too good or too bad to be true, it probably is.
DeSantis's claim is a candidate.
Below, a quote from our book.
The very next paragraph of the book suggests what to do when this happens: trace back to the source. This is a key lesson in our course as well, and at the heart of the "think more, share less" mantra that we stress. Don't share the implausible online until you've checked it out.
In science, people tend to be most interested in positive results — a manipulation changes what you are measuring, two groups differ in meaningful ways, a drug treatment works, that sort of thing.
Journals preferentially publish positive results that are statistically significant — they would be unlikely to have arisen by chance if there wasn't something going on.
Negative results, meanwhile, are uncommon.
Knowing that journals are unlikely to publish negative results, scientists don't bother to write them up and submit them. Instead they up buried file drawers—or these days, file systems.
One of our key pieces of advice is to be careful of confirmation bias.
There's a thread going around about how the crop below is what happens when Twitter's use of eye-tracking technology to crop images is fed with data from a misogynistic society. I almost retweeted it. But…
…that story fits my pre-existing commitments about how machine learning picks up on the worst of societal biases. So I thought it was worth checking out.
While it would be fish in a barrel to drag this paper as a contribution to the pseudoscience of homeopathy, we'll largely pass on that here. More interestingly, this single paper illustrates quite a few of the points that we make in our forthcoming book.
The first of them pertains to the role of peer review as guarantor of scientific accuracy.