Chebyshev inequality

Chebyshev's inequality is a statistical rule that tells us how data values tend to spread around the average (mean). It states that, regardless of the data's distribution, at least \(1 - \frac{1}{k^2}\) of the data falls within \(k\) standard deviations from the mean. For example, with \(k=2\), at least 75% of the data is within two standard deviations of the average. This helps us estimate the variability and how much data we can expect to be close to the average, even without knowing the exact shape of the data distribution.