Also this week: against pi, statistical populations as useful (or not-so-useful) fictions, weasel words about causality, the public face of your scholarly discipline, confidence intervals vs. vitamin D, and more.
Theoretical physicist Stephen Hawking, perhaps the world’s greatest living scientist, and surely its most famous, passed away this week. He was 76. Diagnosed with motor neurone disease at 21 and told he had two years to live, he went on to predict that black holes could emit radiation, a stunning claim with deep implications for fundamental physics. The prediction was later confirmed. He made many other seminal contributions to physics and cosmology, and wrote the popular science bestseller A Brief History of Time. In his later years, using a wheelchair to get around and speaking with the aid of a computer, he became one of the few scientists instantly recognizable to a wide swath of the general public. He played himself in tv shows and movies, and was the subject of the biopic The Theory of Everything.
Data Colada on how the notion of a statistical population, characterized by fixed but unknown population parameters, is (sometimes) a useful fiction. If you want to estimate the true average height of the students in your statistics class, that’s a perfectly well-defined population from which we can take a random sample just like the textbooks say. But the true average effect of N enrichment on plant species richness doesn’t exist, because there’s no well-defined population (not even an infinite one) of studies of N enrichment on plant species richness, from which we could randomly sample.
An oldie but a goodie: what is the public face of your academic discipline, as represented by what bookstores display on their shelves (or these days, what Amazon displays in response to search queries)? For instance, for “history” in the US the answer is “WW II and the Civil War”. For ecology, the answer presumably is “environmentalism”.
Sticking with Kieran Healy: a very good short tweetstorm on using weasel words to hint at causal claims without actually committing to them. He’s talking about sociology, but how often do you think this is done in ecology?
Still sticking with Kieran Healy: what statistical software would the founding fathers of sociology have used? 🙂 Sample:
What makes a scientific theory “interesting”? A classic discussion. From the social sciences, but it generalizes. (ht Andrew Gelman)
Here’s a great example of a misunderstanding of the confidence interval of a sample mean, made by people with advanced degrees and with potentially-serious public health consequences. Good fodder for an intro stats course; I used it in my intro biostats course this week.
Against pi. (ht Matt Levine)