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Cake day: July 2nd, 2023

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  • The funny thing is that Spez didn’t even come up with the idea of Reddit. Him and Ohanian showed up with some other dumb idea and Paul Graham was like, that’s stupid, try this idea of mine.

    Then Spez put together some shitty code in Lisp of all languages, which Aaron Schwartz had to rewrite in Python. The site was so unstable that Spez slept with his laptop so he could reboot it in the middle of the night.

    They used sock puppets because nobody was posting enough content. Then they cashed out and fucked off, leaving Aaron to rewrite it, and Erik Martin to actually make a proper community out of it.

    Then Spez comes riding back into town over a decade later to claim credit while he fucks the whole site up by selling out.

    Truly a masterclass in incompetent white male privilege.





  • Good example and well explained. We should team up on a book on science for lay people!

    Your point about specifying the null hypothesis and the p value is very important. Another way studies can fail is if you pick 20 different variables, like you mentioned, and then look to see if any of them give you p<0.05. So in your example, we measure smiling and 19 other factors besides being told jokes. Let’s say the weather, the day of the week, what color clothes the person is wearing, what they had for breakfast, etc. Again, due to statistics, one of those 20 is going to appear relevant by chance. You’re essentially doing 20 experiments in one so again you’ll get one spurious result that you can report as “success”.

    Experimental design is tough and it’s hard to grok until you’ve had to design and run your own experiment including the math. That makes it easy for people to pass off bad science as legitimate, whether accidentally or on purpose. And it’s why peer review is important, where your study gets sent to another researcher in your field for critique before publication.

    There’s other things besides bad math that can trip you up like correlation vs causation, and how the data is gathered. In the above example, you might try to save money by asking subjects to self report on their smiling. But people are bad at doing that due to fallible memory and bias (did that really count as a full smile?). Ideally you want to follow them around and count yourself, with a clear definition of what counts as a smile. Or make them wear a camera that does facial recognition. But both of those cost more money than just handing someone a piece of paper and a pencil and hoping for the best. That’s why you should always be extra suspicious of studies that use self reporting. As my social psych prof said, surveys are the worst form of data collection. It’s what makes polling hard because what people say and what they do are often entirely different things.




  • P<0.05 means the chance of this result being a statistical fluke is less than 0.05, or 1 in 20. It’s the most common standard for being considered relevant, but you’ll also see p<0.01 or smaller numbers if the data shows that the likelihood of the results being from chance are smaller than 1 in 20, like 1 in 100. The smaller the p value the better but it means you need larger data sets which costs more money out of your experiment budget to recruit subjects, buy equipment, and pay salaries. Gotta make those grant budgets stretch so researchers will go with 1 in 20 to save money since it’s the common standard.


  • P<0.05 means one in 20 studies are relevant just by chance. If you have 20 researchers studying the same thing then the 19 researchers who get non significant results don’t get published and get thrown in the trash and the one that gets a “result” sees the light of day.

    Thats why publishing negative results is important but it’s rarely done because nobody gets credit for a failed experiment. Also why it’s important to wait for replication. One swallow does not make a summer no matter how much breathless science reporting happens whenever someone announces a positive result from a novel study.

    TL;DR - math is hard