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> Similarly, detailed knowledge about advanced statistical methods is a great thing. We have to keep in mind, though, that nothing in the article is advanced s

> Similarly, detailed knowledge about advanced statistical methods is a great thing. We have to keep in mind, though, that nothing in the article is advanced statistical methods. They're the very, very beginning. Being able to run a simple linear regression or bootstrap a data set is equivalent to being able to write fizzbuzz for a programmer.

mlthoughts2018 8y ago
I’d be more inclined to say that understanding expected value and variance at a baseline level is like fizzbuzz of statistics. Basic regression at least requires understanding sampling standard error of estimators, distinguishing p-values from the posterior probability a hypothesis is true, transformation of random variables (like for understanding why classical 95% confidence interval bounds are +/- 1.96 * standard error) and some linear algebra. But I agree the methods discussed, with the exception maybe of support vector machines, are all elementary statistical methods. What’s sad is that if these represent the fizzbuzz of statistics, it means most jobs in statistics only require you to do tasks easier than fizzbuzz (cursory data cleaning & summary stats). Because in most jobs, dimensionality reduction, SVMs, clustering and boosting would be seen as incredibly advanced, scarce work projects for people to fight over who gets to work on them.
- fwdpropaganda 8y ago
  I object to the comparison entirely. Basic statistical concepts are orders of magnitude more complex than a loop with a couple of ifs. As a software engineer you CAN'T fail fizzbuzz. Whereas in statistics I can come up with problems that rely only on basics but are still counter-intuitive enough that some practitioners will make mistakes.
  - madhadron 8y ago
    I used to work as a statistician. Now I work as a software engineer. The comparison is valid between what is considered the basics to operate as a professional.

mlthoughts2018 8y ago

I’d be more inclined to say that understanding expected value and variance at a baseline level is like fizzbuzz of statistics. Basic regression at least requires understanding sampling standard error of estimators, distinguishing p-values from the posterior probability a hypothesis is true, transformation of random variables (like for understanding why classical 95% confidence interval bounds are +/- 1.96 * standard error) and some linear algebra. But I agree the methods discussed, with the exception maybe of support vector machines, are all elementary statistical methods. What’s sad is that if these represent the fizzbuzz of statistics, it means most jobs in statistics only require you to do tasks easier than fizzbuzz (cursory data cleaning & summary stats). Because in most jobs, dimensionality reduction, SVMs, clustering and boosting would be seen as incredibly advanced, scarce work projects for people to fight over who gets to work on them.

fwdpropaganda 8y ago
I object to the comparison entirely. Basic statistical concepts are orders of magnitude more complex than a loop with a couple of ifs. As a software engineer you CAN'T fail fizzbuzz. Whereas in statistics I can come up with problems that rely only on basics but are still counter-intuitive enough that some practitioners will make mistakes.
- madhadron 8y ago
  I used to work as a statistician. Now I work as a software engineer. The comparison is valid between what is considered the basics to operate as a professional.