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The art of Pi (`\pi`), Phi (`\phi`) and `e`
Numbers are a lot of fun. They can start conversations—the interesting number paradox is a party favourite: every number must be interesting because the first number that wasn't would be very interesting! Of course, in the wrong company they can just as easily end conversations.
I debunk the proof that `\pi = 3` by proving, once and for all, that `\pi` can be any number you like!
Periodically I receive kooky emails from people who claim to know more. Not more than me—which makes me feel great—but more than everybody—which makes me feel suspicious. A veritable fount of crazy is The Great Design Book, Integration of the Cosmic, Atomic & Darmic (Dark Matter) Systems by R.A. Forde.
Look at the margin of error. Archimedes' value for `\pi` (3.14) is an approximation — not an exact value. Would you accept an approximation or errors for your bank account balance? Then, why do you accept it for `\pi`? What else may be wrong? —R.A. Forde
What else may be wrong? Everything!
religion—the original roundoff error
Here is a "proof" I recently received that π = 3. The main thrust of the proof is that "God said so." QED? Not quite.
Curiously the proof was sent to me as a bitmap.
Given that it claims to show that π has the exact value of 3, it begins reasonably humbly—that I "may find this information ... interesting." Actually, if this were true, I would find this information staggering.
what's wrong with wrong math?
Because mathematics is the language of physical reality, there's only that far that you can go with wrong math. If you build it based on wrong math, it will break.
Given that math is axiomatic and not falsifiable, its arguments are a kind of argument from authority—the authority of the axioms. You must accept the axioms for the rest to make sense.
Religion also makes its arguments from authority—a kind of divine authority by proxy—though its "axioms" are nowhere as compelling nor its conclusions useful. Normally, the deception in religion's arguments from authority is not obvious. The arguments have been inocculated over time—amgiguity, hedging and the appeal to faith—to be immune to criticism.
When these arguments include demonstrably incorrect math, the curtain falls. The stage, props and other machinery of the scheme becomes apparent. Here you can see this machinery in action. Or, should I say, inaction.
no, π is not 3
If you're 5 years-old: (1) draw a reasonably good circle, (2) lay out a piece of string along the circle and measure the length of the string (circumference), (3) measure the diameter of the circle, (4) divide circumference by diameter. You should get a value close to the actual value of π = 3.14. If you're older, read on.
The book purports "real" (why the quotes?) life experiments to demonstrate that that π is 3. I'll take a look at one below, since it makes use of a coffee cup and I don't like to see coffee cups besmirched through hucksterish claims.
What appears below is a critique of a wrong proof. It is not a proof that `\pi` = 3.
The proof begins with some horrendous notation. But, since notation has never killed anyone (though frustration is a kind of death, of patience), let's go with it. We're asked to consider the following equation, which is used by the proof to show that `\pi = 3`. $$ \sin^{-1} \Delta \theta^c = \frac{\pi}{6} \frac{\theta^{\circ}}{y}\tag{1} $$
where $$ \begin{array}{l} \Delta \theta^c = \frac{2\pi}{12} & \theta^{\circ} = \frac{360^\circ}{12} & y = \frac{1}{2} \end{array} $$
At this point you might already suspect that we're asked to consider a statement which is an inequality. The proof might as well have started by saying "We will use `6 = 2\pi` to show that `\pi = 3`." In fact, this is the exact approach I use below prove that `\pi` is any number. But let's continue with examining the proof.
Nothing so simple as equation (1) should look so complicated. Let's clean it up a little bit. $$ \sin^{-1} a = \tfrac{\pi}{3} b\tag{2} $$
where $$ \begin{array}{l} a = \frac{2\pi}{12} & b = \frac{360^\circ}{12} \end{array} $$
The fact that we're being asked to take the inverse sine of a quantity that is explicitly indicated to be an angle should make you suspicious. Although an angle is a dimensionless quantity and we can write $$ \sin^{-1}(\pi \; \text{rad}) = \sin^{-1}(\pi) = 0 $$
using an angle as an argument to `\sin()` suggests that we don't actually know what the function does.
If we go back to (2) and substitute the values we're being asked to use, $$ \sin^{-1} \tfrac{\pi}{6} = \tfrac{\pi}{3} 30 = 10 \pi \tag{3} $$
we get $$ 0.551 = 31.416 \tag{4} $$
That's as good an inequality as you're going to get. An ounce of reason would be enough for us to stop here, backtrack and find our error. Short of that, we press ahead to see how we can manipulate this to our advantage.
In the next step, the proof treats the left-hand side as a quantity in radians—completely bogus step, but let's go with it—and converts it to degrees to obtain $$ 0.551 \times \tfrac{360}{2 \pi} = 31.574 $$
Yes, we just multiplied only one side of equation (4) by a value that is not one. Sigh.
After committing this crime, the proof attempts to shock you into confusion by stating that $$ 31.574 \neq 31.416 $$
And, given that these numbers aren't the same—they weren't the same in equation (4) either, so the additional bogus multiplication by \(\tfrac{360}{{2 \pi}}\) wasn't actually needed‐the proof states that this inequality must be due to the fact that we used the wrong value for `\pi` in equation (1).
The proof fails to distinguish the difference between an incorrect identity (e.g. `1 = 2` is not correct) and the concept of a variable (e.g. `1 = 2 x` may be correct, depending on the value of `x`). Guided by the dim headlamp of unreason, it suggests that we right our delusion that `\pi = 3.1415...` and instead use `\pi = 3` in equation (1), we get $$ sin^{-1} \tfrac{1}{2} = 30 $$
which is true, because `\sin(30^\circ) = \tfrac{1}{2}`. Therefore, `\pi = 3`.
what just happened?
The entire proof is bogus because it starts with an equality that is not true. In equation (1), the left hand side is not equal to the right hand side.
a simpler wrong proof
To illustrate explicitly what just happened, here's a proof that `\pi = 4` using the exact same approach.
proof that π = 4
Consider the equation, $$ 4 = \pi \tag{5} $$
if we substitute the conventionally accepted value of `\pi` we find $$ 4 = 3.1415... $$
which isn't true! But if we use `\pi = 4` then $$ 4 = 4 $$
which is true! Therefore, `\pi = 4`. QED.
This only demonstrated that I'm an idiot, not that `\pi = 4`.
proof that π is any number you like
But why stop at 4? Everyone can have their own value of `\pi`. In equation (5) in the above "proof", set 4 to any number you like and use it to prove that `\pi` is any number you like.
Isn't misunderstanding math fun?
litany of horrors
The history of the value of π is rich. There is good evidence for `\pi = (16/9)^2` in the Egyptian Rhind Papyris (circa 1650 BC). Archimedes (287-212 BC) estimated `\pi \approx 3.1418` using the inequality `\tfrac{223}{71} \lt \pi \lt \tfrac{22}{7}`
One thing is certain, the precision to which the number is known is always increasing. At this point, after about 12 trillion digits.
So, it might seem, that `\pi \approx 3` is ancient history. Not to some.
Approximations are fantastic—they allow us to get the job done early. We use the best knowledge available to us today to solve today's problems. Tomorrow's problems might require tomorrow's knowledge—an improvement in the approximations of today.
`\pi = 3` is an approximation that is about 2,000 years old (not the best of its time, either). It's comical to consider it as today's best knowledge.
don't bring coffee cups into it
One of the "real" life experiments proposed in the book (pp. 65–68) uses a coffee cup. The experiment is a great example in failing to identify your wrong assumptions.
First you take measurements of your coffee cup. The author finds that the inner radius is `r = 4 cm` and the depth is `d = 8.6 cm`. Using the volume of a cylinder, the author finds that the volume is either `412.8 \; \mathrm{cm}^3 \ 14.0 \mathrm \; {fl.oz}` if `\pi=3` or `432.3 \; \mathrm{cm}^3 = 14.6 \mathrm \; {fl.oz.}` if `\pi=3.14...`.
You're next instructed to full up a measuring cup to 14.6 fl.oz. (good luck there, since measuring cups usually come in 1/2 (4 fl.oz) or 1/3 (2.6 fl.oz) increments).
The author supposedly does this and finds that he could fill the cup to the brim using only 13.7 fl.oz, with the remaining 0.9 fl.oz. spilling.
And now, for some reason, he concludes that this is proof that `\pi = 3`, despite that when using this value of `\pi` the cup's volume was calculated to be 14 fl.oz. not 13.7 fl.oz.
Other than being sloppy, it's most likely that the original assumption that the inside of the coffee cup is a perfect cylinder is wrong. The inside of the cup is probably smooth and perhaps even slightly tapered. Using the maximum radius and depth dimensions will yield a volume larger than the cup's. This is why water spilled out.
Propensity score weighting
The needs of the many outweigh the needs of the few. —Mr. Spock (Star Trek II)
This month, we explore a related and powerful technique to address bias: propensity score weighting (PSW), which applies weights to each subject instead of matching (or discarding) them.
Kurz, C.F., Krzywinski, M. & Altman, N. (2025) Points of significance: Propensity score weighting. Nat. Methods 22:1–3.
Happy 2025 π Day—
TTCAGT: a sequence of digits
Celebrate π Day (March 14th) and sequence digits like its 1999. Let's call some peaks.
Crafting 10 Years of Statistics Explanations: Points of Significance
I don’t have good luck in the match points. —Rafael Nadal, Spanish tennis player
Points of Significance is an ongoing series of short articles about statistics in Nature Methods that started in 2013. Its aim is to provide clear explanations of essential concepts in statistics for a nonspecialist audience. The articles favor heuristic explanations and make extensive use of simulated examples and graphical explanations, while maintaining mathematical rigor.
Topics range from basic, but often misunderstood, such as uncertainty and P-values, to relatively advanced, but often neglected, such as the error-in-variables problem and the curse of dimensionality. More recent articles have focused on timely topics such as modeling of epidemics, machine learning, and neural networks.
In this article, we discuss the evolution of topics and details behind some of the story arcs, our approach to crafting statistical explanations and narratives, and our use of figures and numerical simulations as props for building understanding.
Altman, N. & Krzywinski, M. (2025) Crafting 10 Years of Statistics Explanations: Points of Significance. Annual Review of Statistics and Its Application 12:69–87.
Propensity score matching
I don’t have good luck in the match points. —Rafael Nadal, Spanish tennis player
In many experimental designs, we need to keep in mind the possibility of confounding variables, which may give rise to bias in the estimate of the treatment effect.
If the control and experimental groups aren't matched (or, roughly, similar enough), this bias can arise.
Sometimes this can be dealt with by randomizing, which on average can balance this effect out. When randomization is not possible, propensity score matching is an excellent strategy to match control and experimental groups.
Kurz, C.F., Krzywinski, M. & Altman, N. (2024) Points of significance: Propensity score matching. Nat. Methods 21:1770–1772.
Understanding p-values and significance
P-values combined with estimates of effect size are used to assess the importance of experimental results. However, their interpretation can be invalidated by selection bias when testing multiple hypotheses, fitting multiple models or even informally selecting results that seem interesting after observing the data.
We offer an introduction to principled uses of p-values (targeted at the non-specialist) and identify questionable practices to be avoided.
Altman, N. & Krzywinski, M. (2024) Understanding p-values and significance. Laboratory Animals 58:443–446.
Depicting variability and uncertainty using intervals and error bars
Variability is inherent in most biological systems due to differences among members of the population. Two types of variation are commonly observed in studies: differences among samples and the “error” in estimating a population parameter (e.g. mean) from a sample. While these concepts are fundamentally very different, the associated variation is often expressed using similar notation—an interval that represents a range of values with a lower and upper bound.
In this article we discuss how common intervals are used (and misused).
Altman, N. & Krzywinski, M. (2024) Depicting variability and uncertainty using intervals and error bars. Laboratory Animals 58:453–456.