# Reject Summary Statistics

A summary statistic is what you get when you reduce a bunch of numbers down to a single number:

But it doesn’t have to be the *mean*, it could be:

- median / percentile / IQR
- mode
- min / max / range
- variance / standard deviation
- etc…

Wikipedia says:

[…] summary statistics are used to summarize a set of observations, in order to communicate the largest amount of information as simply as possible.

## Why are summary statistics bad?

Summary statistics are extremely lossy.

They take *all* the data – full of nuances, patterns, outliers, and special cases – and
collapse it down to a single point.

What if the people you know included a basketball player?

Yes: `178.6`

is bigger than `177.4`

… but it doesn’t begin to tell the real story:

## If you don’t look at the data, you don’t understand the data…

It’s almost a cliché, but Anscombe’s quartet clearly shows the limits of summary statistics:

Those four data sets have the same:

- mean (both x and y)
- variance (both x and y)
- correlation
- linear regression

A more recent and striking example is the datasaurus dozen:

## A terrible example: “average” request duration

If you’ve ever been asked:

What’s the average request duration?

for an HTTP endpoint, you know there’s only one good answer:

urgh…

The truth is that “it’s complicated” and the question itself is based on many wrong assumptions:

- the distribution is normal … it’s not
- the distribution has one mode … probably not, depending on caching, or the specific if-then-else handling, etc…
- the mean is “exact” … rather than using a confidence interval

Here is a real duration graph distribution:

Details:

- this is a density plot, a “smoothed” histogram
- yes, this is from a real production system
- it spans thousands of requests over a 1-hour window
- durations were cropped <= 250ms
- the mean duration is 62.4ms (56ms for the cropped data) – raw data

The mean duration sits exactly **nowhere** interesting or representative.

## A failure of communication

Every time I hear/read the word “average”, I assume the worst. The average, or any summary statistics, obscures the real data, incompetently at best or maliciously at worst.

Summary statistics *feel* like information, but they are usually sound bites.

As a society, we need to strive for facts, for understanding, and for objective data:

- REJECT summary statistics: ask for the data if it’s missing
- refuse conclusions until the data and methodology are produced and reviewed
- raise the bar: ask for visualizations – but visualizations do NOT replace the data, they complement the data