Chebyshev's theorem

Chebyshev's theorem is a statistical principle that applies to any dataset, regardless of its distribution. It states that for any dataset, at least \(1 - \frac{1}{k^2}\) of the values will fall within \(k\) standard deviations from the mean (average). For example, if you set \(k=2\), at least 75% of the data points will be within two standard deviations of the mean. This theorem highlights how data is spread around the average, ensuring that we have a basic understanding of variability in any dataset.