Chebyshev's Inequality is a statistical theorem that provides a bound on the probability that a random variable deviates from its mean. It states that for any distribution with a finite mean and variance, the proportion of observations that lie within k standard deviations from the mean is at least $$1 - \frac{1}{k^2}$$ for any k > 1. This inequality is particularly useful because it applies to all distributions, regardless of their shape, making it a powerful tool in probability and statistics.
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