dynomight
I’m fascinated that caffeine is so well-established (the most popular drug?) and yet these kinds of self-experiments still seem to add value over the scientific literature.
Anyway, I have a suspicion that tolerance builds at different rates for different effects. For example, if you haven’t had any caffeine in a long time (like months), it seems to create a strong sense of euphoria. But this seems to fade very quickly. Similarly, with prescription stimulants, people claim that tolerance to physical effects happens gradually, but full tolerance never develops for the effect on executive function. (Though I don’t think there are any long-term experiments to prove this.)
These different tolerances are a bit hard to understand mechanistically: Doesn’t caffeine only affect adenosine receptors? Maybe the body also adapts at different places further down the causal chain.
Nursing doubts
(Many months later) Thanks for this comment, I believe you are right! Strangely, there do seem to be many resources that list them as being hydrogen bonds (e.g. Encyclopedia Brittanica: https://www.britannica.com/science/unsaturated-fat which makes me question their editorial process.) In any case, I’ll probably just rephrase to avoid using either term. Thanks again, wish I had seen this earlier!
Thanks, any feedback on where the argument fails? (If anywhere in particular.)
Datasets that change the odds you exist
I would dissuade no one from writing drunk, and I’m confident that you too can say that people are penguins! But I’m sorry to report that personally I don’t do it by drinking but rather writing a much longer version with all those kinds of clarifications included and then obsessively editing it down.
Do you happen to have any recommended pointers for research on health impacts of processed food? It’s pretty easy to turn up a few recent meta reviews, which seems like a decent place to start, but I’d be interested if there were any other sources, particularly influential individual experiments, etc. (It seems like there’s a whole lot of observational studies, but many fewer RCTs, for reasons that I guess are pretty understandable.) It seems like some important work here might never use the word “processing”.
If I hadn’t heard back from them, would you want me to tell you? Or would that be too sad?
Seed oils are usually solvent extracted, which makes me wonder, how thoroughly are they scrubbed of solvent, what stuff in the solvent is absorbed into the oil (also an effective solvent for various things), etc
I looked into this briefly at least for canola oil. There, the typical solvent is hexane. And some hexane does indeed appear to make it into the canola oil that we eat. But hexane apparently has very low toxicity, and—more importantly—the hexane that we get from all food sources apparently makes up less than 2% of our total hexane intake! https://www.hsph.harvard.edu/nutritionsource/2015/04/13/ask-the-expert-concerns-about-canola-oil/ Mostly we get hexane from gasoline fumes, so if hexane is a problem, it’s very hard to see how to pin the blame on canola oil.
It’s a regression. Just like they extrapolate backwards to (1882+50=1932) using data from 1959, they extrapolate forwards at the end. (This is discussed in the “timelines” section.) This is definitely a valid reason to treat it with suspicion, but nothing’s “wrong” exactly.
Many thanks! All fixed (except one that I prefer the old way.)
Thoughts on seed oil
As the original author of underrated reasons to be thankful (here), I guess I can confirm that tearing apart the sun for raw materials was not an intended implication.
I think matplotlib has way too many ways to do everything to be comprehensive! But I think you could do almost everything with some variants of these.
ax.spines['top'].set_visible(False) # or 'left' / 'right' / 'bottom' ax.set_xticks([0,50,100],['0%','50%','100%']) ax.tick_params(axis='x', left=False, right=False) # or 'y' ax.set_ylim([0,0.30]) ax.set_ylim([0,ax.get_ylim()[1]])
Good point regarding year tick marks! I was thinking think that labeling 0°C would make the most sense when freezing is really important. Say, if you were plotting historical data on temperatures and you were interested in trying to estimate the last frost date in spring or something. Then, 10°C would mean “twice as much margin” as 5°C.
One way you could measure which one is “best” would be to measure how long it takes people to answer certain questions. E.g. “For what fraction of the 1997-2010 period did Japan spend more on healthcare per-capita than the UK?” or “what’s the average ratio of healthcare spending in Sweden vs. Greece between 2000 and 2010?” (I think there is an academic literature on these kinds of experiments, though I don’t have any references on hand.)
In this case, I think Tufte goes overboard in saying you shouldn’t use color. But if the second plot had color, I’d venture it would win most such contests, if only because the y-axis is bigger and it’s easier to match the lines with the labels. But even if I don’t agree with everything Tufte says, I still find him useful because he suggests different options and different ways to think about things.
Thanks, someone once gave me the advice that after you write something, you should go back to the beginning and delete as many paragraphs as you can without making everything incomprehensible. After hearing this, I noticed that most people tend to write like this:
Intro
Context
Overview
Other various throat clearing
Blah blah blah
Finally an actual example, an example, praise god
Which is pretty easy to correct once you see it!
Hey, you might be right! I’ll take this as useful feedback that the argument wasn’t fully convincing. Don’t mean to pull a motte-and-bailey, but I suppose if I had to, I’d retreat to an argument like, “if making a plot, consider using these rules as one option for how to pick axes.” In any case, if you have any examples where you think following this advice leads to bad choices, I’d be interested to hear them.
I think you’re basically right: Correlation is just one way of measuring dependence between variables. Being correlated is a sufficient but not necessary condition for dependence. We talk about correlation so much because:
We don’t have a particularly convenient general scalar measure of how related two variables are. You might think about using something like mutual information, but for that you need the densities not datasets.
We’re still living in the shadows of the times when computers weren’t so big. We got used to doing all sorts of stuff based on linearity decades ago because we didn’t have any other options, and they became “conventional” even when we might have better options now.
2 votes
Overall karma indicates overall quality.
1 vote
Agreement karma indicates agreement, separate from overall quality.
My impression was that the backlash you’re describing is causally downstream of efforts by public health people to promote breastfeeding (and pro-breastfeeding messages in hospitals, etc.) Certainly the correlation is there (https://www.researchgate.net/publication/14117103_The_Resurgence_of_Breastfeeding_in_the_United_States) but I guess it’s pretty hard to prove a strict cause.