Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca - contact me Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca on Twitter Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca - Lumondo Photography Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca - Pi Art Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca - Hilbertonians - Creatures on the Hilbert Curve
Drive, driven. Gave, given.YelloGive me a number of games.more quotes

pi: exciting


DNA on 10th — street art, wayfinding and font


visualization + design

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The 2019 Pi Day art celebrates digits of `\pi` with hundreds of languages and alphabets. If you're a kid at heart—rejoice—there's a special edition for you!

The art of Pi (`\pi`), Phi (`\phi`) and `e`


Pi Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2019 `\pi` has hundreds of digits, hundreds of languages and a special kids' edition.

Pi Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2018 `\pi` day

Pi Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2017 `\pi` day

Pi Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2016 `\pi` approximation day

Pi Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2016 `\pi` day

Pi Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2015 `\pi` day

Pi Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2014 `\pi` approx day

Pi Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2014 `\pi` day

Pi Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2013 `\pi` day

Pi Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Circular `\pi` art

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!

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Willing to fight against unreason? Curious about the luminous and wary of the supernatural? If so, you might want to substitute Hitchmas for Christmas—it comes earlier and there's scotch.

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.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The 'proof' that π is exactly 3. (zoom)

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.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The actual 'proof' from the handwritten book (pp. 18-19), where 'The inaccuracy of its value manifests itself'. Hmhmm. (zoom)

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 constitutes the right proof of the fact that the original proof is wrong. 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.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Don't abuse your coffee cup this way. (zoom)

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.

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news + thoughts

Hola Mundo Cover

Sat 21-09-2019

My cover design for Hola Mundo by Hannah Fry. Published by Blackie Books.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Hola Mundo by Hannah Fry. Cover design is based on my 2013 `\pi` day art. (read)

Curious how the design was created? Read the full details.

Markov Chains

Tue 30-07-2019

You can look back there to explain things,
but the explanation disappears.
You'll never find it there.
Things are not explained by the past.
They're explained by what happens now.
—Alan Watts

A Markov chain is a probabilistic model that is used to model how a system changes over time as a series of transitions between states. Each transition is assigned a probability that defines the chance of the system changing from one state to another.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Markov Chains. (read)

Together with the states, these transitions probabilities define a stochastic model with the Markov property: transition probabilities only depend on the current state—the future is independent of the past if the present is known.

Once the transition probabilities are defined in matrix form, it is easy to predict the distribution of future states of the system. We cover concepts of aperiodicity, irreducibility, limiting and stationary distributions and absorption.

This column is the first part of a series and pairs particularly well with Alan Watts and Blond:ish.

Grewal, J., Krzywinski, M. & Altman, N. (2019) Points of significance: Markov Chains. Nature Methods 16:663–664.

1-bit zoomable gigapixel maps of Moon, Solar System and Sky

Mon 22-07-2019

Places to go and nobody to see.

Exquisitely detailed maps of places on the Moon, comets and asteroids in the Solar System and stars, deep-sky objects and exoplanets in the northern and southern sky. All maps are zoomable.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
3.6 gigapixel map of the near side of the Moon, annotated with 6,733. (details)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
100 megapixel and 10 gigapixel map of the Solar System on 20 July 2019, annotated with 758k asteroids, 1.3k comets and all planets and satellites. (details)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
100 megapixle and 10 gigapixel map of the Northern Celestial Hemisphere, annotated with 44 million stars, 74,000 deep-sky objects and 3,000 exoplanets. (details)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
100 megapixle and 10 gigapixel map of the Southern Celestial Hemisphere, annotated with 69 million stars, 88,000 deep-sky objects and 1000 exoplanets. (details)

Quantile regression

Sat 01-06-2019
Quantile regression robustly estimates the typical and extreme values of a response.

Quantile regression explores the effect of one or more predictors on quantiles of the response. It can answer questions such as "What is the weight of 90% of individuals of a given height?"

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Quantile regression. (read)

Unlike in traditional mean regression methods, no assumptions about the distribution of the response are required, which makes it practical, robust and amenable to skewed distributions.

Quantile regression is also very useful when extremes are interesting or when the response variance varies with the predictors.

Das, K., Krzywinski, M. & Altman, N. (2019) Points of significance: Quantile regression. Nature Methods 16:451–452.

Background reading

Altman, N. & Krzywinski, M. (2015) Points of significance: Simple linear regression. Nature Methods 12:999–1000.

Analyzing outliers: Robust methods to the rescue

Sat 30-03-2019
Robust regression generates more reliable estimates by detecting and downweighting outliers.

Outliers can degrade the fit of linear regression models when the estimation is performed using the ordinary least squares. The impact of outliers can be mitigated with methods that provide robust inference and greater reliability in the presence of anomalous values.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Analyzing outliers: Robust methods to the rescue. (read)

We discuss MM-estimation and show how it can be used to keep your fitting sane and reliable.

Greco, L., Luta, G., Krzywinski, M. & Altman, N. (2019) Points of significance: Analyzing outliers: Robust methods to the rescue. Nature Methods 16:275–276.

Background reading

Altman, N. & Krzywinski, M. (2016) Points of significance: Analyzing outliers: Influential or nuisance. Nature Methods 13:281–282.

Two-level factorial experiments

Fri 22-03-2019
To find which experimental factors have an effect, simultaneously examine the difference between the high and low levels of each.

Two-level factorial experiments, in which all combinations of multiple factor levels are used, efficiently estimate factor effects and detect interactions—desirable statistical qualities that can provide deep insight into a system.

They offer two benefits over the widely used one-factor-at-a-time (OFAT) experiments: efficiency and ability to detect interactions.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Two-level factorial experiments. (read)

Since the number of factor combinations can quickly increase, one approach is to model only some of the factorial effects using empirically-validated assumptions of effect sparsity and effect hierarchy. Effect sparsity tells us that in factorial experiments most of the factorial terms are likely to be unimportant. Effect hierarchy tells us that low-order terms (e.g. main effects) tend to be larger than higher-order terms (e.g. two-factor or three-factor interactions).

Smucker, B., Krzywinski, M. & Altman, N. (2019) Points of significance: Two-level factorial experiments Nature Methods 16:211–212.

Background reading

Krzywinski, M. & Altman, N. (2014) Points of significance: Designing comparative experiments.. Nature Methods 11:597–598.