Measurement noise, correlation and averaging
For repeated indications, the mean has variance Var(x̄)=(1/n²)Σ_iΣ_j Cov(x_i,x_j).
Key knowledge
For repeated indications, the mean has variance Var(x̄)=(1/n²)Σ_iΣ_j Cov(x_i,x_j). It reduces to σ²/n for independent equal-variance observations; shared offsets, temporal correlation and drift can prevent the usual square-root improvement from averaging.
Values and equations
Var(x̄)=(1/n²)Σ_iΣ_j Cov(x_i,x_j)
- x_i,x_j,x̄
- Readings and average; measured quantity unit
- Cov,Var
- Covariance/variance; squared quantity unit
- n
- Reading count
Conditions and scope
- Finite variances and a defined averaging window; Σ_ij denotes covariance of readings i,j, so the sum runs over matrix entries, not squares.
- Instrument bandwidth and sample spacing determine temporal correlation. Reducing high-frequency noise does not remove bias or validate long-term stability.
Sources · 2
References
Sources retain their recorded scope. A source link alone does not establish verification of every statement.
National Institute of Standards and Technology (NIST)2 sources
Evidence and review
NIST TN 1297 (1994): Type A evaluation: Replicate-mean formula and correlated-observation caution; general covariance extension follows linear propagation. NIST TN 1297 (1994): Propagation law: Covariance terms for a linear mean; equal sensitivity 1/n gives the displayed sum.
2026-10-10 · Independent AI editorial review: Independent root AI editorial review of all29 authored Measurement claims/conditions, displayed equations and37 origin relations. The original26 claims were read against41 primary source sections; all5 requested corrections and the3 affected source bindings were subsequently rechecked. Three actual scalar-nonlinear/vector/dynamic applications were read, independently differentiated or integrated, and checked against the specific additional source material described below. This is reference-content review, not human specialist certification, instrument certification or experimental replication. Full-field sign-off waits for the combined final conceptual matrix. Receipt: measurement-independent-content-review.1.
Entry details
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