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The art of Pi (`\pi`), Phi (`\phi`) and `e`
Like music with numbers? Here's a short list of some of my favourite songs that have numbers in their lyrics. Absolutely none of them is about bottles of beer.
music with numbers
1 — One Hundred Billion Sparks, Max Cooper | Imagine a neuron firing. Now imagine 100,000,000,000 neurons firing. This is the music for it.
2 — Numbers, Smoke City | This song is entirely composed of references to different numbers. The bonus? It's both in English and Portuguese. I love the way it ends—"Isn't it beautiful out here?".
Un
Un
Four
Five
Fifteen
Quinze
Seventeen
Seven
...
Tres
forty nine
Isn't it beautiful out here?
Isn't it beautiful out here?
Isn't it beautiful out here?
3 — 99 luftbaloons, Nena | The numerical classic.
Hast du etwas Zeit für mich
Dann singe ich ein Lied für dich
Von 99 Luftballons
4 — One, Aimee Mann | Beautiful mathematics of relationships using small numbers.
One is the lonliest number that you'll ever do. Two can be as bad as one, it's the lonliest number since the number one.
5 — Angels at My Door, Una | Long sequences of numbers.
58, 56, 54 angels at my door.
63, 62, 61, 60, 59, 58 angels at my gate.
6 — Tricky, Tricky, Royksopp | Number words about the fearful six from Norway.
Six afraid of seven 'cause seven, eight, nine
I'm about to lose it the second time
7 — Pt vs Ys, Yoshinori Sunahara | The first four numbers, in German, are this song's lyrics.
Eins, zwei, drei, vier.
8 — Der Kommissar, Falco | Unlike the previous song, this one starts with a German count (doesn't get to vier, though) and just gets better from there.
Two, three, four
Eins, zwei, drei
Na, es is nix dabei
Na, wenn ich euch erzähl' die G'schicht'
9 — 2wicky, Hooverphonic | The numbers likely reference the Prophet-600 and SH-101 synthesizers.
Prophet 60091.
Before we start you should know that you're not the only one who can hurt me.
SH10151.
This is the serial number of our orbital gun.
SH10151.
You better be sure before you leave me for another one.
10 — Straight to Number One, Touch and Go | Something to listen to after midnight.
Fingers, four, play, three, to number one.
11 — The Beat Experience, Pepe Deluxe | I am reminded of this song at too many academic seminars.
Here we are now, at the middle, of the fourth large part of this talk.
More and more I have the feeling that we are getting nowhere.
Slowly, as the talk goes on, we are getting nowhere.
And that is a pleasure.
It is not irritating where one is.
It is only irritating to think one would like to be somewhere else.
Here we are now, a little bit after the middle, of the fourth large part of this talk.
12 — Thousand Kisses Deep, Leonard Cohen | A list of songs that doesn't include one by Cohen is not worth reading. The sentiment of a thousand kisses is as old as lips existed. Catullus wrote to Lesbia "da mi basia mille, deinde centum, dein mille altera, dein secunda centum, deinde usque altera mille, deinde centum" [Give me a thousand kisses, then a hundred, then another thousand, then a second hundred, then yet another thousand, then a hundred.] Well, you get the idea.
And sometimes when the night is slow,
The wretched and the meek,
We gather up our hearts and go,
A Thousand Kisses Deep.
13 — Six Seven Times, Flunk | Curiously the product here is 42. This song is the answer to life.
You've got it all
Six seven times
You've got it all
Makes me feel so fine
And it's all there is
14 — 7 seconds, Youssou N'Dour | Dreamy references to a short period of time.
7 seconds away. Just as long as I stay. I'll be waiting.
15 — 100 Billion Stars, Lux | Something to relax to while you ponder insignificance.
16 — First Picture, Nikolaj Grandjean | First is the most memorable number.
I remember
The first picture
One million different shadows
Where we've been around the willows
17 — Millions of Millions, Music for a French Elevator | Very desirable. And I can't believe I transcribed the whole thing.
5.50 million dollars, 2.6775 and very desirable 8 million dollars 5.6 million and 2.4 million 3.4 million and 2.9 million 1.2 would've amounted to 4 million 1.2 million 19.4 million 6.6 million 5.275 million 1.2 million 3-and-a-half million dollars 6.453 million 8 million 5.050 million 1.4 million close to a million dollars 933.5 million 3.8 million 5 million dollars 2-and-a-half million 600 million dollars can you shut the door? 3-and-a-half million dollars 2.5 million .050 million 572,750 thousand 5.050 million 3.8 million 3 million 150 thousand 8 million 419.5 million
18 — Love Potion #9, The Searchers | I started kissing everythying in sight.
It smelled like turpentine, it looked like Indian ink
I held my nose, I closed my eyes, I took a drink.
19 — 93 Million Miles, Luan Santana feat. John Kip | A little sticky, a little sweet but it makes up for the fact that much of it is in Portuguese.
But the absence of the light is a necessary part.
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.