## Disambiguation

“Indicator function” can mean different things depending on where you read about it:

- In
**probability (set theory)**: A random variable for an event that equals 1 when the event happens and 0 when the event does not happen. - In
**statistics**: A synonym for a characteristic function, which completely defines a probability distribution.

According to Professor Greg Lawler of the University of Chicago^{1}, the term “indicator function is the first definition: a random variable that takes on a value of 0 or 1.

“The corresponding function in analysis is often called the characteristic function and denoted χE. Probabilists never use the term characteristic function for the indicator function because the term characteristic function has another meaning. The term indicator function has no ambiguity.” ~ Greg Lawler.

This article is about the indicator function as used in set theory. For use in probability distributions, see: What is a characteristic function?

## What is an Indicator Function in Probability?

Indicator functions are useful “switches”, simplifying probability statements to “Yes” or “No” statements. The concept similar to how an if() statement works in computer programming.

The indicator function, I_{a} (or I_{E}) is defined on the whole space

(which is the universal set in set theory). This simple function has just two values: 0 and 1.

- 0 = the event happens,
- 1 = the event does not happen.

For example, let’s say you were interested in knowing how many people voted for Alvin Brown for mayor of Jacksonville. A “1” means the event happened (they voted for him), a “0” means the event didn’t happen (they did not vote for him).

## Indicator Functions and Expectation

**The expectation of the indicator function for an event is the probability of that event. **

*Why? *The expected value for a random variable is the sum of the events multiplied by their probabilities. For example, if you wanted to find the expected value for rolling a single die, the expected value is:

E(X) = x * P(x) = 1(1/6) + 2(1/6) + 3(1/6) + 4(1/6) + 5(1/6) + 6(1/6) = 3.5.

In the same way, the expectation for an indicator function is also the sum of the probabilities. In notation, that’s:

The two probabilities involved with indicator functions are: the event happens (1), or the event doesn’t happen (0), giving:

If an event *E* doesn’t happen, it’s called the event’s complement, denoted* E ^{c},* which means our equation can now become:

Finally, the equation reduces to:

**References:**

(1) Lawler, G. (2016). Probability Notes.

**Need help with a homework or test question? **With Chegg Study, you can get step-by-step solutions to your questions from an expert in the field. Your first 30 minutes with a Chegg tutor is free!

*Statistical concepts explained visually* - Includes many concepts such as sample size, hypothesis tests, or logistic regression, explained by Stephanie Glen, founder of StatisticsHowTo.

**Comments? Need to post a correction?** Please post a comment on our *Facebook page*.