You've probably seen how this looks for economics before. There's a large excess of p-values that are just shy of the significance threshold.
But that's nothing. Economics hasn't committed anywhere near the level of sinning medicine has.
Medicine isn't even the worst offender and they're already this bad.
Since nutrition is what got me interested in this, here's how they fare:
Sorry about the scale, but that's just what p-hacking does. It's just that bad.
Now part of this may be down to economists reporting way more tests, and way more values, so their literature doesn't look as bad. But they still tend to focus on the marginal results that pile up near 0.05 even if they publish a bunch of less dodgy p-values.
No field is safe.
On the subject of p-hacking, this might be one of my favorite pictures showing how it works out: researchers don't report unless p < 0.05 and they prefer positive results.
- Cross-sectionally sig for parent edu and peer achievement at wave 1, age ~16
- Only cs sig for peer achievement at age ~22
- Only longitudinally sig for peer achievement
- Maybe parent edu moderation in non-URM pops
for Wilson effect into adulthood. But, hard to reconcile with lack of peer effects on means in this data. Analysis possibly confounded by IQ level moderation of heritability * age effects.
- no family income Scarr-Rowe
Once again, the most important aspect of SES is parent edu.
The test used had a reliability of 0.75-0.80, so it's worse than most cog tests, but using the uncorrected wave-averaged results, we get this addition to the Burt estimates plot:
So MZ twins were within ~1 point of the same person retesting.
If we break out Korean versus non-Korean international adoptees, the results wrt edu are the same but Korean adoptees slightly outperform the average Swede and non-Korean international adoptees do not.
The low-high parent education gaps are not significant among adoptees.
But the power to detect the general population effect size is >0.999 in both adoptee samples. If there's a causal impact of parental education on kids' IQ scores, it's overestimated if you take the general population estimate for granted.
If you aren't already, you should be following Amir. He's an excellent researcher who works with Nordic registry data and his within-family studies are incredibly high-quality and full of interesting facts.
In this one, he seems to have killed the idea epidurals cause ASD/ADHD.
In another large Finnish sample, with a total of 690,654 births, he found that the risk from maternal smoking during pregnancy also disappeared within families.
Thanks to all of my 6,165 followers for following me. In a few minutes, my account will officially be 30 days old.
To mark the date, here's a thread of previous threads I've done and charts I've made to catch up new followers.
My first post was on ideal versus realized fertility. I found that the gap between women's actual and ideal numbers of kids grew with smarts. In other words, the smartest women were the most likely to have an unsatisfactorily low number of kids.
At the end of that thread, I posted fertility data for various groups in Denmark. As the chart showed, fertility has decreased for every group in the country, so now the most fertile - by a slim margin - are native Danes.
So you have some twin data and you want to do some interpreting. If you don't have access to raw data, it's easy to do some approximating to get a feel for the similarity or dissimilarity of people at different degrees of relatedness.
Here's code for a hypothetical dataset.
In this data, the reliability leads to a personal difference of 5.35 points between testings. For corrected MZ, DZ, half-sib, and adoptees, you'd get 8.67, 12.27, 14.53, and 15.04 points. Assuming unrelated people are uncorrelated, they tend to vary by 16.93 points.
This approximation works really well, and the higher the correlation, the better it performs. I invite you all to simulate it.
Here are four plots showing that it works well with sample sizes of 100,000, 1,000, 250, and even 100.