Chebyshev's Inequality is a statistical theorem that provides a bound on the probability that a random variable deviates from its mean. This inequality states that for any real number $k > 1$, at least $1 - \frac{1}{k^2}$ of the values of a dataset lie within $k$ standard deviations of the mean. It connects expected value and variance by emphasizing how spread out values can be in relation to these statistical measures.
congrats on reading the definition of Chebyshev's Inequality. now let's actually learn it.