Expectation of a continuous random variable#
Let \(X\) be a discrete random variable taking real values. The expectation of \(X\) is defined by:
where \(p(x)\) is the PDF of \(X\).
Example: The expectation of a uniform random variable#
Take a uniform random variable:
Its expectation is:
which makes a lot of sense because it is the point between \(a\) and \(b\).
Interpretation of the expectation of continuous random variables#
Mathematically, the expectation is the x-coordinate of the centroid of the area between the x-axis and the PDF, see Fig. 9.

Fig. 9 The expectation of a random variable is the x-coordinate of the centroid of the area between the x-axis and the PDF.#
As in the discrete case, you can think of the expectation as the value one should “expect” to get. But, again, take this interpretation with a grain of salt… See Fig. 10 for an example where the expectation is a very improbable value.

Fig. 10 The expectation of a random variable does not have to be on a high probability region.#
Note
In Fig. 10, the PDF of the random variable has two distinct peaks. Each peak of the PDF is called a mode. “Nice” PDF’s have a single mode. They are called unimodal. For unimodal PDF’s the interpretation that the expectation is the value you should expect to get makes a lot of sense. However, a PDF may be multi-modal. Then the expectation is not very useful…