Accuracy vs Precision

I had a hard time remembering this1, almost as bad as Type 1 vs Type 2 (which are still the worst terminology I’ve ever seen).

Thinking of these by reframing them with actual statistical terms made them easy to remember.


A random variable (the estimator) is accurate if it has low bias.


A random variable is precise if it has low variance.


From these definitions, it’s finally clear to me why neither implies each other. Say your true distribution is a standard () Gaussian. Consider these cases for your estimator, also a Gaussian with the following parameters:


Perfectly accurate because it’s unbiased. Imprecise as hell.


Inaccurate. Very precise.


Inaccurate and imprecise.


Perfectly accurate, more precise than the true distribution because its spread is lower.

  1. 4 years until 10 minutes ago, when I finally thought about it properly. 

Related Posts

How I feel about ebooks

List of places where the US has been involved in regime change, with multiplicity

Handy command line benchmarking tool

Stan Rogers

Ultimate Hot Couch Guy

Quote on Java Generics

The Programmer Tendency

Figure out undocumented JSON with gron

Mental Model of Dental Hygiene

Book Review: Swastika Night