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Comment by klodolph | Hacker News Reader

> If they can’t get the statistics right, how can we expect it from teachers with far less education?

Because it’s their job—and if the statistics curriculum is mandatory, then the teachers might spend inservice days developing statistics curriculum, going to workshops to learn how to teach statistics, etc. Teachers will develop statistics curriculums and share them with each other. With NSF grants, you can fund teacher outreach programs, put statistics exhibits in science museums, etc.

A lot of teachers will struggle to teach various subjects. That’s why we have support networks in place, to help develop curriculums and provide training for teachers. I know that the support network has a lot of problems—but many teachers would struggle to teach, say, biology, history, or algebra, too, without support.

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I think what's far more likely is that the curriculum-writers will see it as an opportunity to dumb down the math. They'll make it into a very basic course on study design and data collection, with a handful of descriptive statistics thrown in so the students have something to do with their scientific calculator's STAT mode.

Will they teach discrete and continuous probability distributions? The binomial, Poisson, and normal distributions? Dependent and independent random variables? Bayes' Theorem? Measures of statistical significance and hypothesis testing? Chi-squared and student-t distributions? Confidence intervals and p-values? Maximum likelihood estimation?

Highly doubtful, since many of those topics are built on top of university-level calculus.

People who have some understanding of study design and data collection would be in a much better spot to understand and interpret day-to-day news / “information flood” than those who have done a lot of calculus-based probability. You can go all the way through rigorous measure-theoretical probability and come away with almost nothing useful for interpreting a study.

Most problems I see with moderns statistics aren’t of the form “ohhh, they fooled you by using a subtly wrong statistical metric to ascribe significance” but “the way the data was gathered/interpreted is fundamentally wrong and made to mislead”

Some of the problems come from statistics itself. “All models are wrong, but some are damn hard to interpret.” Let’s make that a corollary to Box’s statement about the utility of models. Why does so much statistical analysis take place without even an expectation of human comprehension? It’s just magical sigils for most papers.

Many midlevel statistical practitioners suffer from a holier than thou complex, where a “correct” approach to statistical analysis might buy a little more precision at the expense of a lot of comprehension.

Box plots or Bar charts with error bars, using randomized data collection. That’s like 90% of the interpretive value right there. Statistics is a UI for math and it could use improvement if we expect so much from it.

See Brett Victor’s “Kill Math” for more context on why we should expect more from our mathematical interfaces. http://worrydream.com/KillMath/

> "Math" consists of assigning meaning to a set of symbols, blindly shuffling around these symbols according to arcane rules, and then interpreting a meaning from the shuffled result. The process is not unlike casting lots.

Form that Brett Victor.

Wow

@dr_dshiv Edit: Sorry about the bad quote, I should have included the prefix "When most people speak of Math ...". And the negative comment about the blog post.

Now I've read / skimmed all of it and it was interesting, I hope the new methods he wants to use for teaching maths will work fine. (I'm sceptical, but still seems like worth to give it a try.)

I think his project title does his project a disservice: "Kill maths"? That sounds silly to me. And how it starts -- I got annoyed and stopped reading (until I went back two days later).

Another more positive project title maybe could be "Maths for everyone" or "A new approach for teaching maths"?

In my experience they (try to) teach those somewhat. Not in a calculus-type rigor, or even very mathematically, but conceptually yes.

The problem is that those things are not "immediately" needed, so students don't learn them or immediately forget them if they do. What students "immediately need" is to run some test in some application and check if some value passes some magical threshold.

These students then become researchers and these researchers become professors.

>I think what's far more likely is that the curriculum-writers will see it as an opportunity to dumb down the math.

I'd go as far as to argue that has already happened. The reason math doesn't really teach problem solving and instead opts for working through things with formulas etc is precisely because that's a dumbed down version of math.

This is kind of like blank slate thinking though.

Not all topics are equally easy to teach and assess in a deep way, with limited time and resources.

Statistics is IMO a lot like security. Unless it's at a very high level, just follow a basic check list and don't do anything creative. Calculus is more like algorithms - you can get to deep and creative levels at an earlier stage.

A big problem is that people do creative stuff but use the basic check list.

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