Gauss's inequality

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In probability theory, Gauss's inequality (or the Gauss inequality) gives an upper bound on the probability that a unimodal random variable lies more than any given distance from its mode.

Let X be a unimodal random variable with mode m, and let τ 2 be the expected value of (X − m)2. (τ 2 can also be expressed as (μ − m)2 + σ 2, where μ and σ are the mean and standard deviation of X.) Then for any positive value of k,

\Pr(\mid X - m \mid > k) \leq \begin{cases}
\left( \frac{2\tau}{3k} \right)^2 & \text{if } k \geq \frac{2\tau}{\sqrt{3}} \\[6pt]
1 - \frac{k}{\tau\sqrt{3}}        & \text{if } 0 \leq k \leq \frac{2\tau}{\sqrt{3}}.

The theorem was first proved by Carl Friedrich Gauss in 1823.

See also[edit]


  • Gauss, C. F. (1823). "Theoria Combinationis Observationum Erroribus Minimis Obnoxiae, Pars Prior". Commentationes Societatis Regiae Scientiarum Gottingensis Recentiores 5. 
  • Upton, Graham; Cook, Ian (2008). "Gauss inequality". A Dictionary of Statistics. Oxford University Press. 
  • Sellke, T.M.; Sellke, S.H. (1997). "Chebyshev inequalities for unimodal distributions". American Statistician (American Statistical Association) 51 (1): 34–40. doi:10.2307/2684690. JSTOR 2684690. 
  • Pukelsheim, F. (1994). "The Three Sigma Rule". American Statistician (American Statistical Association) 48 (2): 88–91. doi:10.2307/2684253. JSTOR 2684253.