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> People with these names are more likely than others to have these professions. Shouldn't it say: "People with these names happen to be in those professions m

> People with these names are more likely than others to have these professions. Shouldn't it say: "People with these names happen to be in those professions more often than others"? Anyway, there are a couple of fun ones in there, but I'll let you figure those out yourself. Unfortunately neither my name nor my profession are covered — I'm not quite sure what to make of that. :/ --- They use the same language in their blog post: "Arnolds therefore appear to have a much higher tendency to be accountants than Shanes." That's just wrong, no? By wrong I just mean intentionally misleading. I'm sure that has nothing to do with the fact that they sell an app that helps you find names for you babies though.

colanderman 12y ago
EDIT2: OK folks we're smart, let's use MATH. Take above quote, which compares "1.9% of Arnolds are accountants" to the "0.55% of Shanes [are accountants]". They're implying that the probability of being an accountant (J), given that ones name (N) is Arnold, is above the expected probability of being an accountant in general. So they're looking for a high P[J|N]/P[J]. Now compare with what we were expecting to see. We assumed the chart showed, for a given job, names which had a higher incidence than normal. i.e., we're looking for a high P[N|J]/P[N]. Guess what. P[J|N]/P[J]=P[N|J]/P[N] by Bayes' Theorem [1]. These are EXACTLY the same metric! So their technique, and the chart, is correct. (And my original post, below, was wrong.) (Not saying anything about causation here, and I don't think they were either.) [1] http://en.wikipedia.org/wiki/Bayes%27_theorem http://en.wikipedia.org/wiki/Bayes%27_theorem ----------- Yes, that is completely backward. 99% of Arnolds could go into farming, yet still the 1% who go into accounting dominate that field, and hence show up on this chart. EDIT: but you missed the first half of that quote: "In our sample of two and a half million people, a whopping 1.9% of Arnolds are accountants. Contrast that with just 0.55% of Shanes." So I think the quote is correct. Makes me wonder if their chart is backward. (i.e., they put "Arnold" under "Accountant" because Arnolds are likely to be accountants, not because accountants are likely to be Arnolds, as the grouping implies).
- stolio 12y ago
  After some pencil work I was convinced of EDIT2, but now I'm confused.. Before we get to my confusion, one interesting thing I found along the way is the following situation, call J1 "job 1" and N1 "name 1", and use the P(N|J)/P(N) (or its equivalent) metric: J1 J2 ------ N1 N1 N1 N1 N1 N3 N2 If we limit each job to it's top name, N1 doesn't get attached to either despite being the most common name in each. J1 gets N2 while J2 gets N3. If this is the method then don't use this chart to guess the names of people in a profession, use it to guess the professions of people whose names you know. "Guy" may be listed for investment bankers, but an investment banker is still more likely to be named Dave, but if you meet a Dave he's likely to be a mechanic. For the same situation say we use the top value for P(N|J), then J1 gets N1 and so does J2. P(J|N) goes back to J1 getting N2 and J2 getting N3 and N1 being left out in the cold. But here's where it's unclear, I think this: > In our sample of two and a half million people, a whopping 1.9% of Arnolds are accountants. Contrast that with just 0.55% of Shanes. Arnolds therefore appear to have a much higher tendency to be accountants than Shanes implies they're listing the top P(J|N) values for each J. *(edit: they're comparing Arnolds to Shanes, not Arnolds to all accountants?) I think your approach is the most consistent but is it what they're using?
adevine 12y ago
It doesn't appear incorrect to me. The blurb on the graph says "For example, a higher percentage of Elwoods are farmers than of most any other name." As I read it, it means that, say, 1% of Elwoods become farmers, while only .1% of Steves are farmers. That is, if your name is Elwood, you are much more likely to be a farmer than if your name is anything else.
- coherentpony 12y ago
  What you're implying here is that correlation implies causation. If 99% of farmers are Elwoods, you can't claim that one's name being Elwood means one is more likely to become a farmer.
  - walterbell 12y ago
    In that scenario, could one claim that "one's name being Elwood means one is more likely to BE a farmer"?
    - teamhappy 12y ago
      Yup, you got it right. The wording is important. Apparently, if your name is Elwood you're more likely to be a farmer today. But that obviously doesn't mean that kids named Elwood are more likely to become farmers (it could, but I'm afraid you're gonna have to proof that). Wikipedia has some simple examples: https://en.wikipedia.org/wiki/Correlation_does_not_imply_causation https://en.wikipedia.org/wiki/Correlation_does_not_imply_cau...
      - madcaptenor 12y ago
        In this case, it probably means that people named Elwood are more likely to (a) be older and (b) live in rural areas, both of which are correlated with being a farmer.
  - stdbrouw 12y ago
    The predictive element itself has little to do with correlation/causation, though. "If you have gray hair, you're more likely to die in the next ten years than people without gray hair" is a perfectly valid statement, and does not imply at all that gray hair is causing people to die. If anything, it's more like an ecological fallacy or unwarranted extrapolation to the future.
Lambdanaut 12y ago
I've heard mentioned in books on psychology before about this effect, and it seems at least from a clinical perspective, that people do tend to choose their professions based on their names. I'm sorry I can't provide sources.
- Retra 12y ago
  We would expect people to also tend to choose their names based on their parents, as well as choosing their jobs based on their upbringing.
  - emodendroket 12y ago
    That's obvious, but I would hope the psychologists purportedly studying this topic thought of that when they designed their experiment.
- jlees 12y ago
  It's mentioned in "Yes! 50 Scientifically Proven Ways to be Persuasive"[1], which despite the title, is a really interesting book. The Freakonomics blog quotes it here: http://freakonomics.com/2009/04/24/yes-part-ii/ http://freakonomics.com/2009/04/24/yes-part-ii/ referencing this paper "Why Susie Sells Seashells by the Seashore: Implicit Egotism and Life Decisions" http://www.stat.columbia.edu/~gelman/stuff_for_blog/susie.pdf http://www.stat.columbia.edu/~gelman/stuff_for_blog/susie.pd... [1]http://www.amazon.com/Yes-Scientifically-Proven-Ways-Persuasive/dp/1416570969 http://www.amazon.com/Yes-Scientifically-Proven-Ways-Persuas...

colanderman 12y ago

EDIT2: OK folks we're smart, let's use MATH. Take above quote, which compares "1.9% of Arnolds are accountants" to the "0.55% of Shanes [are accountants]". They're implying that the probability of being an accountant (J), given that ones name (N) is Arnold, is above the expected probability of being an accountant in general. So they're looking for a high P[J|N]/P[J]. Now compare with what we were expecting to see. We assumed the chart showed, for a given job, names which had a higher incidence than normal. i.e., we're looking for a high P[N|J]/P[N]. Guess what. P[J|N]/P[J]=P[N|J]/P[N] by Bayes' Theorem [1]. These are EXACTLY the same metric! So their technique, and the chart, is correct. (And my original post, below, was wrong.) (Not saying anything about causation here, and I don't think they were either.) [1] http://en.wikipedia.org/wiki/Bayes%27_theorem http://en.wikipedia.org/wiki/Bayes%27_theorem ----------- Yes, that is completely backward. 99% of Arnolds could go into farming, yet still the 1% who go into accounting dominate that field, and hence show up on this chart. EDIT: but you missed the first half of that quote: "In our sample of two and a half million people, a whopping 1.9% of Arnolds are accountants. Contrast that with just 0.55% of Shanes." So I think the quote is correct. Makes me wonder if their chart is backward. (i.e., they put "Arnold" under "Accountant" because Arnolds are likely to be accountants, not because accountants are likely to be Arnolds, as the grouping implies).

stolio 12y ago
After some pencil work I was convinced of EDIT2, but now I'm confused.. Before we get to my confusion, one interesting thing I found along the way is the following situation, call J1 "job 1" and N1 "name 1", and use the P(N|J)/P(N) (or its equivalent) metric: J1 J2 ------ N1 N1 N1 N1 N1 N3 N2 If we limit each job to it's top name, N1 doesn't get attached to either despite being the most common name in each. J1 gets N2 while J2 gets N3. If this is the method then don't use this chart to guess the names of people in a profession, use it to guess the professions of people whose names you know. "Guy" may be listed for investment bankers, but an investment banker is still more likely to be named Dave, but if you meet a Dave he's likely to be a mechanic. For the same situation say we use the top value for P(N|J), then J1 gets N1 and so does J2. P(J|N) goes back to J1 getting N2 and J2 getting N3 and N1 being left out in the cold. But here's where it's unclear, I think this: > In our sample of two and a half million people, a whopping 1.9% of Arnolds are accountants. Contrast that with just 0.55% of Shanes. Arnolds therefore appear to have a much higher tendency to be accountants than Shanes implies they're listing the top P(J|N) values for each J. *(edit: they're comparing Arnolds to Shanes, not Arnolds to all accountants?) I think your approach is the most consistent but is it what they're using?

adevine 12y ago

It doesn't appear incorrect to me. The blurb on the graph says "For example, a higher percentage of Elwoods are farmers than of most any other name." As I read it, it means that, say, 1% of Elwoods become farmers, while only .1% of Steves are farmers. That is, if your name is Elwood, you are much more likely to be a farmer than if your name is anything else.

coherentpony 12y ago
What you're implying here is that correlation implies causation. If 99% of farmers are Elwoods, you can't claim that one's name being Elwood means one is more likely to become a farmer.
- walterbell 12y ago
  In that scenario, could one claim that "one's name being Elwood means one is more likely to BE a farmer"?
  - teamhappy 12y ago
    Yup, you got it right. The wording is important. Apparently, if your name is Elwood you're more likely to be a farmer today. But that obviously doesn't mean that kids named Elwood are more likely to become farmers (it could, but I'm afraid you're gonna have to proof that). Wikipedia has some simple examples: https://en.wikipedia.org/wiki/Correlation_does_not_imply_causation https://en.wikipedia.org/wiki/Correlation_does_not_imply_cau...
    - madcaptenor 12y ago
      In this case, it probably means that people named Elwood are more likely to (a) be older and (b) live in rural areas, both of which are correlated with being a farmer.
- stdbrouw 12y ago
  The predictive element itself has little to do with correlation/causation, though. "If you have gray hair, you're more likely to die in the next ten years than people without gray hair" is a perfectly valid statement, and does not imply at all that gray hair is causing people to die. If anything, it's more like an ecological fallacy or unwarranted extrapolation to the future.

Lambdanaut 12y ago

I've heard mentioned in books on psychology before about this effect, and it seems at least from a clinical perspective, that people do tend to choose their professions based on their names. I'm sorry I can't provide sources.

Retra 12y ago
We would expect people to also tend to choose their names based on their parents, as well as choosing their jobs based on their upbringing.
- emodendroket 12y ago
  That's obvious, but I would hope the psychologists purportedly studying this topic thought of that when they designed their experiment.
jlees 12y ago
It's mentioned in "Yes! 50 Scientifically Proven Ways to be Persuasive"[1], which despite the title, is a really interesting book. The Freakonomics blog quotes it here: http://freakonomics.com/2009/04/24/yes-part-ii/ http://freakonomics.com/2009/04/24/yes-part-ii/ referencing this paper "Why Susie Sells Seashells by the Seashore: Implicit Egotism and Life Decisions" http://www.stat.columbia.edu/~gelman/stuff_for_blog/susie.pdf http://www.stat.columbia.edu/~gelman/stuff_for_blog/susie.pd... [1]http://www.amazon.com/Yes-Scientifically-Proven-Ways-Persuasive/dp/1416570969 http://www.amazon.com/Yes-Scientifically-Proven-Ways-Persuas...