Episode 41: Science and Math

This week Zach and Kelly talk about how fear and/or awe of math differs between academic fields.

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SCIENCE

Paper 1: The Nonsense Math Effect

Paper 2: Heavy use of equations impedes communication among biologists

10 thoughts on “Episode 41: Science and Math

  1. Jakub S

    Zach, it may be “utilize” in science, but in the business world it’s “leverage” that is driving me nuts right now.

    Reply
  2. Chris

    I can kind of see why people would rate the abstracts with the equation as higher for different reasons. I think the fact that there was a discrepancy between the math sciences and the social sciences is important. If I were looking up different studies and came across an abstract that was relevant to what I was studying there are a number of different things I would look for. To me the more information given would almost always be a bonus. More technical information would seemingly legitimize the work. However I think it would be interesting to reverse the experiment and instead use a social science terminology or something similar at the bottom instead. I bet most people in the relevant field would notice it was a concept that didn’t really add anything relevant to the abstract. I also would hypothesize that most math or physics majors would think it would add something to the abstract. Its more of a misunderstanding of the information be presented then some sort of awe in regards to math.

    Reply
  3. Bob

    I’m surprised nobody has come up with any guesses as to what the “nonsense” equation in the first paper could mean. It doesn’t look like a differential equation, because the f is subscripted and there’s no /dx.

    The big secret in computer science is that implementing algorithms correctly is pretty easy. Managing complexity is hard.

    Reply
    1. Bob

      Also also, I’m not really “in” computer science. In school, I liked to take random computer science classes, until they started enforcing class prerequisites on me. They had their curriculum divided up into “real” classes and “programming for non-majors” classes, and wouldn’t accept the latter as a prerequisite for anything interesting. So I have to cast my vote against “X for Y” classes, too.

      Reply
  4. Dave

    I know it was such a tiny offhanded comment at the end of the podcast… but (also as a programmer) I lol’ed at the idea that you can tell a good programmer from a bad based on things as simple/objective as correctness and efficiency.

    Wikipedia has a giant list of non-functional qualities that go into “good software”… and very often improving software in one of these areas will damage it in another: http://en.wikipedia.org/wiki/Non-functional_requirement

    Reply
  5. Zhou Fang

    One possible explanation is that theoretical papers usually spawn an ‘applied’ version at some later point. Then people tend to cite those, instead of the original heavily theoretical paper. So, it’s not like the two sides don’t communicate – rather, they tend to do so using intermediaries.

    Reply
  6. Cristian

    The investigation he made could have others interpretations. IT could “prove” the people from social science, literature or humanities, don’t really understood the equation (or care about) and supposed it was related or correct. It could “prove” that they have some kind of irrational-preconcept saying that math equations on social study means direct calculable relations (which are the golden plot of social variables (fake and shinny)) Plus, the data base he use (internet low paid random people) is not good. Why would a professor rate that paper for 0,50? or a doctor? I don’t believe they really were what they say.
    The other study; the best science? Do you believe in empirical progress in the translation from theory to theory? You think that empirical comparison and logic is the cause of science theory change (aka “progress”)?
    We the sociologist need more learning in math, but investigators in general need more epistemology, there are lots of outdated epistemic paradigms staying around.
    Modeling create factors, It doesn’t take them

    Reply
  7. jackson frost

    Just wanted to make a subtle point that you guys may already have identified in retrospect…in arguing against the methodology of Dr Ericsson’s legerdemath :p Zach actually appears to have done a pretty good job providing evidence for the point!

    As Kelly pointed out, of course, the math is just basic algebra…a couple rates and their coefficient factors…and I believe it also stands that, like she pointed out, there was really no possibility of correlating any of the equation’s symbols with any of the important variables discussed in the abstract.

    Zach, it seems, got the equation confused with a second-order differential equation…and in fact, even offered an excuse for those groups of scientists whose members afforded more gravitas to abstracts with the nonsense math! His reaction, which seemed to be something along the lines of, “oh, this fancy-looking math could probably apply here, and differential equations are pretty useful for modelling lots of things!” was probably the same line of reasoning subconsciously employed by the non-STEM scientists who were subject to the “math-awe effect”, — a pretty strong statement on the power of math to have “caught” even someone who knows what the study is about, I should think!!!!

    Just thought it was kind of interesting hearing that line of reasoning play out in real time on the show! 🙂

    Reply

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