Chebyshev's Inequality is a statistical theorem that provides a way to estimate the proportion of values that lie within a certain number of standard deviations from the mean in any probability distribution, regardless of its shape. It states that for any real-valued random variable with finite mean and variance, at least $$1 - \frac{1}{k^2}$$ of the values fall within $$k$$ standard deviations of the mean for any $$k > 1$$. This inequality is crucial when dealing with continuous random variables, as it allows for conclusions about the distribution without assuming normality.
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