Tchebysheff’s Theorem
Let 𝑌 be a random variable with mean 𝜇 and finite
variance 𝜎 2 . Then, for any constant 𝑘 > 0,
1
𝑃(|𝑌 − 𝜇| < 𝑘𝜎) ≥ 1 − 𝑘 2 or
1
𝑃(|𝑌 − 𝜇| ≥ 𝑘𝜎) ≤ 2
𝑘
Given a number k greater than or equal to 1 and a set of n
1
measurements, at least 1 − (𝑘 2 ) of the measurement will
lie within k standard deviations of the mean.
 It can be applied to any probability distribution,
whether it is bell-shaped or not.
 This is very conservative.
Example 3.28
The number of customers per day at a sales counter, Y, has been
observed for a long period of time and found to have mean 20 and
standard deviation 2. The probability distribution of Y is not
known. What can be said about the probability that, tomorrow, Y
will be greater than 16 but less than 24.
(The answer is at least ¾)