How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Chebyshev's inequality on a finite probability space
Statement
For every finite real random variable and every , Equivalently, if and , then . If , the first form remains valid for every .
Facts & Assumptions
Given: A finite real random variable and a real threshold .
Variance is and (Variance, standard deviation, and covariance on a finite probability space).
Markov's inequality states for nonnegative and (Markov's inequality on a finite probability space).
Proof
The variable is nonnegative, and for the events and are equal.
Apply [L2] to at the positive threshold and use [L1] to obtain the first inequality.
If , substitute in step 2.1 and cancel to obtain . Conversely, given , choose in the standard-deviation form to recover step 2.1. If , step 2.1 gives probability at most zero for every .
Depends on
Used by
- ℙ(X=0)leVar(X)/E[X]² whenever E[X]≠0 Corollary
- An extra square-root sample-size factor destroys standard-error studentization Counterexample
- A studentized rank U-statistic under a weak null Example
- A symmetric two-point distribution attains equality in Chebyshev's inequality Example
- Exact equality null versus weak parameter null Example
- Studentized difference of means with equal means and unequal variances Example
- Chebyshev bounds the majority error of repeated Bernoulli trials Lemma
- Strictly feasible DMC input laws achieve every lower mutual-information rate Lemma
- Typical-projector bounds for finite quantum sources and cq ensembles Lemma
- Estimate an inner product from ℓ2-sampling access Proposition
- The general inequalities compare cleanly with the published finite ones Remark
- Almost-sure Marchenko–Pastur law for finite-fourth-moment sample covariance matrices Theorem
Dependency tree · two levels
7 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- C. M. Grinstead and J. L. Snell, Introduction to Probability, 2nd ed., Theorem 8.1 (standard reference, not scraped)
- H. Pishro-Nik, Introduction to Probability, Statistics, and Random Processes, Section 6.2.2 (standard reference, not scraped)