Math Intuition

Published

September 16, 2025

CautionUnder construction

Mathematics

Updated mental models

Numbers aren’t just a count; a better viewpoint is a position on a line. This position can be negative (\(-1\)), between other numbers (\(\sqrt{2}\)), or in another dimension (\(i\)).

Arithmetic became a general way to transform a number. Addition is sliding along the number line (\(+ 3\) means slide 3 to the right) and multiplication is scaling ( \(\times 3\) means scale it up 3 times).

Mathematically, the exponent function does this:

\[ \mathrm{original} \times \mathrm{growth}^{\mathrm{duration}} = \mathrm{new} \]

or \[ \mathrm{growth}^{\mathrm{duration}} = \frac{\mathrm{new}}{\mathrm{original}} \]

Operation Old concept New concept
Addition Repeated counting Sliding
Multiplication Repeated addition Scaling
Exponents Repeated multiplication Growth for amount of time

Understanding \(e\) (it’s about growth)

The number \(e\) is the base amount of growth shared by all continually growing processes. The number \(e\) merges Rate and Time. When we write:

\[ e^x \]

The variable \(x\) is actually a combination of rate and time.

\[ x = \text{rate} \times \text{time} \]

So, our general formula becomes:

\[ \text{growth} = e^x = e^{r t} \]

The number \(e\) is about continuous growth. Intuitively, \(e^x\) means:

  • How much growth do I get after after \(x\) units of time (and 100% continuous growth)

100% continuous growth means that, at any given time, the rate of change is always equal to our current value. Or more mathy: (d/dx)*(e^x) = e^x

Understanding \(ln()\) (it’s about time)

  • \(e^x\) lets us plug in time and get growth.
  • \(ln(x)\) lets us plug in growth and get the time it would take.

Statistics

  • Probability is starting with an animal, and figuring out what footprints it will make.
  • Statistics is seeing a footprint, and guessing the animal.

https://wetlandscapes.com/blog/a-brief-visualization-of-rs-distribution-functions/

https://wetlandscapes.com/blog/a-brief-visualization-of-rs-distribution-functions/

Common statistical tests are linear models

https://lindeloev.github.io/tests-as-linear/

https://lindeloev.github.io/tests-as-linear/