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Comment by fwdpropaganda | Hacker News Reader

> When I opened this thread, this was downvoted. I can understand why, it's kinda dismissive of the article. However, I can also understand the frustration and would generally agree.

Although I didn't downvote, I often downvote comments that are negative on knowledge. You know the type. If we're talking about CS they will argue that you don't have to know algorithms and data structures to be a good developer, because you can just go on stackexchange. If we're talking about data, they will argue that knowing what a p-value is useless because the methodology is flawed anyway. If we're talking about knowledge itself they will argue that going to university is a waste of time. And of course, no matter the subject matter, you never need "all that maths".

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I would like to know what you consider as knowledge.

Yes, there are lots of developers who do not know algorithms and data structures in depth, yet they get the job done because most jobs simply do not require it.

It's not a universally accepted fact that college helps everyone, it helps some kind of students while others are better off without it.

But i think you simply downvote the comments who you do not agree with.

I don't usually see many comments like this, which "downvote knowledge" as you say.

Instead, I see comments arguing that the emphasis placed on that knowledge is wrong-headed or misplaced.

For example, knowledge about data structures is a great thing. But defining an interview process with data structure trivia as an implicit standard for hiring someone is bad, and is a good teachable moment for pointing out why it's a silly way to filter candidates.

Similarly, detailed knowledge about advanced statistical methods is a great thing. But it's silly to force candidates to fill resumes with buzzwords or treat their stats knowledge like peacock feathers because those are the hoops to get hired, when then in reality the work will be 90% data engineering and devops. It's similarly a teachable moment to point out why an emphasis on these methods is silly, unless you're in a business that actually plans to give an employee projects and tasks that require these things.

I guess if I saw a comment that truly was dismissive of the knowledge, in and of itself, then I would agree that is worth downvoting.

I honestly can say I've not seen that very often, though. Only comments reacting to that way signalling effects are used to subvert the knowledge for e.g. silly hiring trivia or data science credential.

> 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.

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.

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.

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.

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