Linked from

The 13 pages that link to Error propagation, each with the reason it gives.

• Triangle inequalityRelated: The inequality bounds total error by summing contributions from individual errors.

• Sensitivity analysisCompared with: It focuses on propagated errors, while sensitivity can examine broader input influence.

• Measurement errorRelated: Derived quantities inherit error from the measurements used to calculate them.

• Partial derivativeRelated: Partial derivatives weight how each uncertain input affects an output.

• Taylor's theoremRelated: Remainder bounds help estimate errors when nonlinear functions are approximated.

• RoundingRelated: Repeated calculations can carry rounding discrepancies into later results.

• Slide ruleRelated: Approximate readings make chained slide-rule calculations accumulate uncertainty.

• Total derivativeRelated: Total derivatives provide the first-order sensitivities used to propagate small input errors.

• Minkowski inequalityRelated: The inequality bounds the norm of a combined error by separate error norms.

• Precision measurementRelated: Derived quantities inherit uncertainty from every measured input.

• Linear approximationRelated: A derivative-based linear approximation estimates how input errors change an output.

• Local truncation errorRelated: Each step’s local truncation error joins errors already propagated from earlier steps.

• Approximate measureRelated: Calculations can make an approximate measure less certain than its inputs suggest.