You're My Null

by Greg Crowther

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

 Lyrics

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]

 Other Files

MP3 (by Will Crowley)

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.”