Chebyshev's Inequality is a statistical theorem that provides a bound on the probability that the value of a random variable deviates from its mean. Specifically, it states that for any random variable with finite mean and variance, the proportion of observations that lie within k standard deviations of the mean is at least $$1 - \frac{1}{k^2}$$, for any k > 1. This inequality emphasizes the relationship between expectation, variance, and how data spreads around the mean, connecting well with broader concepts in probability and statistics.
congrats on reading the definition of Chebyshev's Inequality. now let's actually learn it.