You're My Null

by Greg Crowther

I wrote this song for my girlfriend-at-the-time, a biostatistician, who subsequently became my wife.

If every gal I’ve met is a hypothesis
Who might turn out to be the mate I’ve sought,
And if I were to yield
To the conventions of my field,
Then you would be referred to as H-naught¹...

CHORUS:
You’re my null,² after all; you’re a theory in the making,³
So robust to every test -- my hypothesis of choice.
You account for the past; you’re a vision of the future;⁴
And when I feel confused, you’re my signal through the noise.⁵

My life has been an uncontrolled experiment⁶ --
A source of all too many scattered plots.⁷
But you provide a line
With an R-squared of point-nine;⁸
Yes, you and you alone connect my dots⁹...

CHORUS

One can never prove a null
In a finite length of time;
Each finding simply strengthens it or not.¹⁰
But if I see trends emerge --
And let’s just say I do --
I’d be crazy not to publish what I’ve got...

CHORUS [twice]

• MP3 (by Will Crowley)

• music video

¹“H-naught” is a way of pronouncing H₀, an abbreviation for the null hypothesis. In research, H₀ is often a “default” position that either will be rejected or not rejected by the data.

²“Null” is often used as shorthand for “null hypothesis.”

³A hypothesis that survives repeated testing may eventually be accepted as a theory.

⁴A good hypothesis both accounts for previous data and makes predictions to be tested in the future.

⁵The signal-to-noise ratio is an engineering concept. In the context of research, the signal may be considered a "true" or important trend that might be obscured by other fluctuations in the data (noise).

⁶A good experiment has a control group, of course.

⁷A "scatter plot" is a common way of graphing the relationship between two variables (e.g., Y versus X).

⁸An R² value close to 1 indicates an excellent fit between a linear model and the data.

⁹Another reference to the fit between a model and the data.

¹⁰Albert Einstein is said to have written, “No amount of experimentation can ever prove me right; a single experiment can prove me wrong.”