Markov and Chebyshev probability bounds
For nonnegative X with finite mean and a>0, P(X≥a)≤E[X]/a.
Key knowledge
For nonnegative X with finite mean and a>0, P(X≥a)≤E[X]/a. For finite-variance Y and t>0, P(|Y−E[Y]|≥t)≤Var(Y)/t², by applying the first bound to (Y−E[Y])².
Conditions and scope
- These bounds require no normality or independence.
- A loose upper bound is not an approximation to the true tail probability.
Sources · 1
References
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Evidence and review
Axler Theorem 4.1 and probability consequence/Exercise 4A.2; Chebyshev substitution independently checked.
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