> Where are sperm samples coming from? Some people give samples because they are sperm donors, others because they are infertile and want to figure out why.
That's about enough to conclude all this doesn't mean much. The only way to have reliable data is to have a great sample selected at random; if the incoming data is not random, but is in fact skewed by factors relevant to the data we're looking for, there's very little value in it.
The rest of this (excellent) article explains why the procedures are inconsistent, geographically as well as historically, and poorly documented, etc., and lists many reasons why the various studies could be flawed. It ends without a definitive conclusion, and saying we don't quite know.
This prudence is admirable, but IMHO the anxiety about sperm count seems largely overblown. I'm not too worried.
Edit: One important thing the article doesn't talk about is how sperm is actually counted. Obviously researchers don't count all 150 to 300 million sperms per sample; they probably count a small (or very small) sub-sample and extrapolate. At least they did when they first started studying this. And maybe today some studies use machines? Or other sophisticated / more precise methods? And others don't? The evolution and diversity of the counting methods is obviously a major factor that needs to be discussed.
> That's about enough to conclude all this doesn't mean much.
You list a common confounder, then skip over the section that says 5/6 studies that have none of these confounders find a decline. That's a fairly important followup.
> However, Auger identify six (of their original seventy) studies that they say are extremely well done and suffer from none of these potential confounders. They say five of the six still show declining sperm counts, and believe that the effect might be real (they say there is no evidence it is global rather than limited to these five regions but I think if an effect happens in five regions, and there is only one region where it is not happening, it is fair to wonder whether it represents a broader trend). I interpret their conclusions as very cautiously pro-decline-hypothesis (Fisch remains against).
> Edit: One important thing the article doesn't talk about is how sperm is actually counted. Obviously researchers don't count all 150 to 300 million sperms per sample; they probably count a small (or very small) sub-sample and extrapolate
When you've got hundreds of millions of something distributed essentially randomly in some space, counting a subsample and extrapolating is fine.
> If for example, they did rough extrapolations in the 50s, and then in the 90s they were able to do an exact and exhaustive count, it's a problem.
That's not a change that's happened, smaller volumes are still analysed, but no that's not necessarily a problem.
At such scales, "small" samples can be totally fine and have an extremely high chance of matching a complete analysis. That's why polling and sampling works.
The problem shrinks even more when you're looking at averages over groups. Smaller samples are noisy, not biased. Averaging over many doesn't lower bias but it does lower the noise.
if the incoming data is not random, but is in fact skewed by factors relevant to the data we're looking for, there's very little value in it
Looking at people who donate sperm effectively is a random sample of men unless there's a causal link between sperm count and the desire to be a donor.
Sampling only people who self-select themselves into one specific group (sperm donors) definitely isn’t a random sample. There are obviously correlations between income, geographic area and age with the desire to become a donor.
Don't sperm banks collect all of that relevant information? I would assume they collect at least age, income, and education. Probably some health history as well.
There can easily be a causal link. It’s a self selected group specifically on sexual reproductive strategy. I don’t need to be able to explain the relationship in detail; it’s enough that it’s plausible and that it hasn’t already been studied.
Personal attacks like this will get you banned here, regardless of how wrong someone is or you feel they are. If you'd please read the rules and stick to them, we'd appreciate it: https://news.ycombinator.com/newsguidelines.html. You may not owe erroneous sperm counters better, but you owe this community better if you're participating in it.
Also, "Please don't sneer, including at the rest of the community." That's in the site guidelines too, because it's reliably a marker of bad comments and worse threads.
It’s not intellectual dishonesty, but a simple matter of how the wage gap is defined. If you take the wage gap simply as the percentage of men’s earnings without correcting for anything, you can then take that measure and see how it changes over time and by region. That’s useful and worthwhile research. But for some reason any mention of the wage gap makes some people so angry that they refuse to look at the actual studies and how they work.
Maybe the reason the phenomenon is hard to account for, is because it's not real. Things that don't exist are difficult to explain.
Maybe we shouldn't spend so much energy trying to find causes when we're not even sure there's an effect. TFA argues, in essence, that metastudies point to severe inconsistencies and are ultimately inconclusive, and I agree with it.
(Also: "armchair analyst" is involuntary funny, because most analysts, unlike generals (or maybe like most generals?) work at a desk.)
That's about enough to conclude all this doesn't mean much. The only way to have reliable data is to have a great sample selected at random; if the incoming data is not random, but is in fact skewed by factors relevant to the data we're looking for, there's very little value in it.
The rest of this (excellent) article explains why the procedures are inconsistent, geographically as well as historically, and poorly documented, etc., and lists many reasons why the various studies could be flawed. It ends without a definitive conclusion, and saying we don't quite know.
This prudence is admirable, but IMHO the anxiety about sperm count seems largely overblown. I'm not too worried.
Edit: One important thing the article doesn't talk about is how sperm is actually counted. Obviously researchers don't count all 150 to 300 million sperms per sample; they probably count a small (or very small) sub-sample and extrapolate. At least they did when they first started studying this. And maybe today some studies use machines? Or other sophisticated / more precise methods? And others don't? The evolution and diversity of the counting methods is obviously a major factor that needs to be discussed.