Combine weighing and volume uncertainty into concentration uncertainty
The tool combines standard uncertainties for formulas such as C=m/V, C=(m·P)/V, and C=(m·P)/(M·V).
Calculate uncertainty for concentration and dilution results from weighing error, volume error, and related inputs directly in the browser.
Review combined standard uncertainty uc, expanded uncertainty U=k·uc, the main contributors, and report-ready copy output in one place.
The tool combines standard uncertainties for formulas such as C=m/V, C=(m·P)/V, and C=(m·P)/(M·V).
It handles certificate-style ±a(k=2), specification-style ±a, and triangular assumptions without making you do the conversion separately.
The contributor breakdown shows which factor dominates the variance so you can target improvements efficiently.
Switch between plain text, Markdown, CSV, and JSON and copy the exact format you need.
m=100.00 mg ±0.10 (rectangular), V=100.00 mL ±0.08 (normal, k=2)
Shows concentration, uc, U, and the contributor split between m and V.
C1=1000 mg/L ±5, V1=10.00 mL ±0.02, V2=100.00 mL ±0.08
Shows U for the diluted concentration and which volume error matters most.
A=98.0 ±0.5, B=100.0 ±0.2
Shows uncertainty for A/B or Recovery(%).
Enter m, P, M, and V together
Shows the contribution from purity and molar mass as well.
The standard-deviation-like uncertainty associated with an input quantity x.
The standard uncertainty of the result y after combining all input contributions.
The report-facing uncertainty calculated as U = k·uc.
A coefficient showing how strongly the result changes when one input changes.
The share of the total variance attributable to one factor.
Standard uncertainty conversion: u=a/k, a/√3, a/√6Combined standard uncertainty: uc = √Σ(c_i·u_i)^2Expanded uncertainty: U = k·ucConcentration: C = m/V, (m·P)/V, (m·P)/(M·V)Dilution: C2 = C1·V1/V2Ratio: R = A/BTolerance notation can be converted into standard uncertainty when you choose a distribution assumption. The tool uses u=a/k for normal(k), u=a/√3 for rectangular, and u=a/√6 for triangular.
k=2 is common, but you should follow your standard, internal rule, or customer requirement. The tool always shows the chosen k in the result.
Yes. Plain text, Markdown, CSV, and JSON are available, and the generated output includes assumptions, inputs, results, and the top contributors.
The first release assumes independent inputs. If your inputs are correlated, the result can be under- or over-estimated.
This tool uses first-order propagation. If relative uncertainties are large or the formula is strongly nonlinear, you should verify the result with another method.