It’s easier to go up a ladder than to come down, literally and metaphorically.

Gian-Carlo Rota made a profound observation on the application of theory.

One frequently notices, however, a wide gap between the bare statement of a principle and the skill required in recognizing that it applies to a particular problem.

This isn’t quite what he said. I made two small edits to generalize his actual statement. He referred specifically to the application of the inclusion-exclusion principle to problems in combinatorics. Here is his actual quote from [1].

One frequently notices, however, a wide gap between the bare statement of the principle and the skill required in recognizing that it applies to a particular combinatorial problem.

This post will expand a little on Rota’s original statement and a couple applications of the more general version of his statement.

## The inclusion-exclusion principle

I don’t think Rota would have disagreed with my edited version of his statement, but it’s interesting to think of his original context. The inclusion-exclusion principle seems like a simple problem solving strategy: you may count a set of things by first over-counting, then correcting for the over-counting.

For example, a few days ago I wrote about a graph created by turning left at the *n*th step if *n* is divisible by 7 or contains a digit 7. Suppose you wanted to count how many times you turn in the first 100 steps. You could count the number of positive integers up to 100 that are divisible by 7, then the number that contain the digit 7, then subtract the number that both are divisible by 7 and contain a 7.

You can carry this a step further by over-counting, then over-correcting for your over-counting, then correcting for your over-correction. This is the essence of the following probability theorem.

The inclusion-exclusion principle a clever idea, but not *that* clever. And yet Rota discusses how this idea was developed over decades into the Möbius inversion principle, which has diverse applications, including Euler characteristic and the calculus of finite differences.

## Bayes’ theorem

Bayesian statistics is a direct application of Bayes’ theorem. Bayes’ theorem is a fairly simple idea, and yet people make careers out of applying it.

When I started working in the Biostatistics Department at MD Anderson, a bastion of Bayesian statistics, I was surprised how subtle Bayesian statistics is. I probably first saw Bayes’ theorem as a teenager, and yet it was not easy to wrap my head around Bayesian statistics. I would think “This is simple. Why is this hard?” The core principle was simple, but the application was not trivial.

## Newtonian mechanics

When I took introductory physics, we would get stuck on homework problems and ask our professor for help. He would almost always begin by saying “Well, *F* = *ma*.”

This was infuriating. Yes, we know *F* = *ma*. But how does that let us solve this problem?!

There’s more to Newtonian mechanics than Newton’s laws, a lot more. And most of it is never made explicit. You pick it up by osmosis after you’ve worked hundreds of exercises.