Only in the same sense in which operating a car requires advanced knowledge of mechanical engineering, electrical engineering, aerodynamics, fluid dynamics, thermodynamics, etc. IOW, in no sense related to the everyday, ordinary practice of operating a car.

Sure, statistics requires calculus... and real analysis, and probability, and measure theory, and FSM knows what else - IF you're doing statistics research or trying to break new ground, or do really advanced things. But all of this is light years away from the level of statistics knowledge needed by Joe Q. Public to better understand (and not be misled by) the "statistics" frequently thrown out in news articles, government reports, etc.

Please, for the love of FSM, can we stop this HN "thing" of assuming that every mention of any mathematical topic implies that the goal of the user/learner is to do original research in the field?

Statistics is more complicated philosophically and computationally, as you do in fact need calculus to do statistics, since a fundamental tool of statistics is evaluating the area under a variety of distribution curves.

> Statistics is more complicated philosophically and computationally, as you do in fact need calculus to do statistics

This is just fundamentally false, as there are many statistical concepts and introductory statistics courses (given at some of the most renowned universities in the world) that can be taught without an underpinning of calculus. It's like arguing that you can't teach a programming class until someone understands digital logic design.

And again, you do need calculus to do statistics. Maybe you're saying: "If someone does the calculus part for you, you can use this one equation to solve this one question."

That's akin to calling a spelling test literature. You are confusing practice with knowledge. Anyone can bang on a drum. That does not make them a drummer.

Discrete distributions can be defined, even infinite discrete distributions (as sequences and series are taught in pre-calc). Continuous distributions can't be formally defined, but a lot of intuition can be given with hand-waving e.g. the area under this curve. Probably most of the class is spent with counting and probability-type problems, but plenty of actual statistics can be done without calculus - we can learn about distributions, what a statistic is, expectation, sampling, counting, probability... the list goes on.

As a professional data scientist I've never needed to use calculus unless you consider graphical reasoning on distribution diagrams to be calculus.

Most people who deal with data/statistics in their working lives do not need to know real analysis, do not utilize calculus to draw (correct) conclusions, and do not use linear algebra either. They learned all these things once and forgot them a long time ago, because they did not need them. They utilize statistics an order of a magnitude more often than they do calculus.

They are not statisticians, but people who need to deal with data as part of their job.

Sure, they may not be able to tell you what is a measurable function, but they can explain the way you should feel about a p-value.

It goes without saying this proposed class wouldn’t be based around memorizing unmotivated formulas.

To go back to the car analogy, I know why I need an engine, you might even say I know how to use the engine, but if the engine dies or I want to use the engine for some other purpose, I'm not equipped to do anything.

I don't have "literacy" with engines, I have rote memorization of a series of steps. I don't have enough information to know why the steps are what they are, nor could I know under what conditions the steps should change or what they should change to.

Will it result in students not knowing as many formulas? Of course, but who cares?

Then what? Are they going to incorrectly use anther one, a wrong one, because they understand it better?

(Searching not where one lost the key, but where the light is stronger)

Hmm, I wonder if you studied stats? It sounds as if you studied lots of math.

I did long ago, mostly forgotten, and, hmm, I don't think there's a single real life useful probability function one can understand, without knowing the underlying calculus? Except for understanding in the sense of trusting the teacher.

Which works pretty ok in medicine

You only need measure theory when working with something that is not easily replaceable by R^n, Z^n, or finite sets to meaningfully define integration, otherwise (in)finite sums and Riemann integration get you very far.

I am a bit rusty on my advanced probability theory, but IIRC the only thing that required* measures was defining conditional probabilities and expected values on zero-probability events.

Of course redoing that class without Lebesgue integration sounds excruciatingly painful.

* Not just to make proofs nicer and theorems more powerful