Were it not for Trump, the great drama of the 2016 election would have been the primary contest between Hillary Clinton and Bernie Sanders. Sanders generally fit the mold of a “leftist protest candidate”, but was far more successful than previous such candidates have been. In this post, I will examine the 2016 American National Election Studies data, hoping to find clues that explain why.
I always thought “braised” meant “simmered for a long time,” but actually it means “cooked once with dry heat, then simmered for any length of time.” For this dish, the tofu was stir-fried until lightly golden, then simmered briefly in thickened broth.
As in many (most?) Asian dishes, the tofu is a “meat replacement” in an economic sense only; it accompanies meat rather than fully replacing it.
The broth was flavored with some ginger, sriracha, and leaks, sauteed with some rice wine, soy sauce, and oyster sauce; for the broth, I used chicken Better Than Bouillon thickened with corn starch. I ended up overdoing the salty/savory a bit, an overcorrection to my previous experiments with Cantonese recipes.
This is a review of three books about low back pain: Crooked, by Cathryn Ramin; Back Mechanic, by Stuart McGill, and the Complete Guide to Low Back Pain, an book-length article by Paul Ingraham from PainScience.com (paywalled.)
These are three very different books, written by three very different authors:
- Cathryn Ramin is an investigative journalist who suffers from severe low back pain.
- Stuart McGill is a Ph.D. kinesiologist at the University of Waterloo.
- Paul Ingraham is a science writer and massage therapist, but more importantly for our purposes, he’s a empiricist nerd obsessed with evidence-based medicine.
[Epistemic status: I’m teaching myself Bayesian analysis out of an O’Reilly-esque programming book; I haven’t yet mustered myself to crack the intimidating Andrew Gelman tome on my shelf. I beg you, correct me if I have screwed this up.]
As part of my quest to finally understand the differences between Bayesian analysis and frequentist analysis, I downloaded his data and poked at it with PyMC, again modeling my analyses after those in chapter 2 of Bayesian Methods for Hackers, by Cameron Davidson-Pilon (the A/B testing example and the Challenger example.)
- It’s okay to tell my wife about the affair.
- It’s okay to not tell my wife about the affair.
- It’s okay to ask me for $50,000.
- But it’s not okay to condition the choice between (1) and (2) on (3); that’s blackmail.