Hypothesis testing leads to more scientific nonsense than any other practice, including fraud. Hypothesis testing, as regular readers know, cannot identify cause. It conflates decision with probability and leads to vast, vast over-certainties.

Why is it so liked? Two reasons. One, it is magic. When the wee p shows itself after the incantation of an algorithm, it is as if lead has been transmuted into gold, dross into straw. Significance has been found! Two, it saves thinking. Wee ps say are taken to mean the cause—or “link”, which everybody takes as “cause”—that was hoped for has been certified.

What is “significance”? A wee p. And what is a wee p? Significance.

And that is it.

Here’s the headline: Premature Birth Rates Drop in California After Coal and Oil Plants Shut Down: Within a year of eight coal- and oil-fired power plant retirements, the rate of preterm births in mothers living close by dropped, finds new study on air pollution..

Shutting down power plants that burn fossil fuels can almost immediately reduce the risk of premature birth in pregnant women living nearby, according to research published Tuesday. Researchers scrutinized records of more than 57,000 births by mothers who lived close to eight coal- and oil-fired plants across California in the year before the facilities were shut down, and in the year after, when the air was cleaner. The study, published in the American Journal of Epidemiology, found that the rate of premature births dropped from 7 to 5.1 percent after the plants were shuttered, between 2001 and 2011. The most significant declines came among African American and Asian women. Preterm birth can be associated with lifelong health complications.

Now this is a reporter, therefore we cannot expect her to know not to use causal language. The peer-reviewed study is “Coal and oil power plant retirements in California associated with reduced preterm birth among populations nearby” by Joan Casey and six other women.

The journal editors, all good peer reviewed scientists, surely know the difference between cause and correlation though, yes?

No. For in the same issue the paper ran appeared an editorial praising the article in causal terms. The editorial was from Pauline Mendola. She said, “We all breathe.”

Who knew?

She also said “Casey and colleagues have shown us that retiring older coal and oil power plants can result in a significant reduction in preterm birth and that these benefits also have the potential to lower what has been one of our most intractable health disparities.”

Casey did not show this. Casey found wee p-values in (we shall soon see) an overly complicated statistical model. Casey found a correlation, not a cause. But the curse of hypothesis testing is that everybody assumes, while preaching the opposite, that correlation is causation.

Onto Casey.

One would assume living near power plants, and even near recently closed power plants, we’d find folks unable to afford the best obstetrical services, and that we’d also find “disparities”—always a code word for differences in races, etc. So we’d expect differences in birthing. That’s causal talk. But with excellent evidence behind it.

Casey’s Table 1 says 7.5% of kids were preterm whose mothers’ address was near a power plant. They called this address the “exposure variable“. These power plants were all over California (see the news article above for a map).

Casey & Co. never measured any effect of any power plant—such as “pollution” or PM2.5 (i.e. dust), or stray electricity, or greater power up time, or etc. Since Casey never measured anything but an address, but could not help but go on about pollution and the like, the epidemiologist fallacy was committed. This is when the thing blamed for causing something is never measured and when hypothesis testing (wee p-values) are used to assign cause.

Anyway, back to that 7.5% out of 316 births. That’s with power plant. Sans plant it was 6.1% out of 272. Seems people moved out with the plants. But the rate did drop. Some thing or things caused the drop. What?

Don’t answer yet. Because we also learn that miles away from existent plants the preterm rate was 6.2% out of 994, while after plant closings it was 6.5% out of 1068. That’s worse! It seems disappearing plants caused an increase in preterm babies! What Caly needs to do is to build more plants fast!

Dumb reasoning, I know. But some thing or things caused that increase and one of the candidates is the closed plants—the same before and after.

So how did Casey reason it was plant removal that caused—or was “linked” to—preterm babies to decrease? With a statistical model (if you can find their paper, see their Eq. [1]). The model not only included terms for plant distance (in buckets), but also “maternal age (linear and quadratic terms), race/ethnicity, educational attainment and number of prenatal visits; infant sex and birth month; and neighborhood-level poverty and educational attainment.”

Linear and quadratic terms for mom’s age? Dude. That’s a lot of terms. Lo, they found some of the parameters in this model evinced wee ps, and the rest of the story you know. They did not look at their model’s predictive value, and we all know by now that reporting just on parameters exaggerates evidence.

Nevertheless they concluded:

Our study shows that coal and oil power plant retirements in California were associated with reductions in preterm birth, providing evidence of the potential health benefits of policies that favor the replacement of oil and coal with other fuel types for electricity generation. Moreover, given that effect estimates were stronger among non-Hispanic Black women, such cleaner energy policies could potentially not only improve birth outcomes overall but also reduce racial disparities in preterm birth.

Inappropriate causal language and politics masked as science. Get ready for a lot more of this, friends.

Share this: Facebook

Reddit

Twitter

Pinterest

Email

More

Tumblr

LinkedIn



WhatsApp

Print



