Percentage Error Calculator: Measuring Accuracy

Worked example

Say a lab experiment expects a boiling point of 200°C but the thermometer reads 195°C. Enter 200 as the actual/expected value and 195 as the measured value. The formula is |(measured - actual) / actual| × 100: |(195 - 200) / 200| × 100 = |(-5) / 200| × 100 = 0.025 × 100 = 2.5%. Since the measured value (195) is lower than expected (200), the tool adds a line noting the measurement is (1 - 195/200) × 100 = 2.5% lower than expected — which matches the main error figure exactly in this case, because there's only one direction of deviation to describe.

Why the formula divides by the actual value, not the measured value

Percentage error always uses the accepted/true value as the denominator, never the observed one — this keeps the metric consistent across repeated trials where the true value stays fixed but the measurement varies. Swapping the denominator would make the same absolute error read as a different percentage depending on which run happened to measure high or low.

How to read the result

The tool bands its plain-language interpretation: under 1% is called excellent accuracy, 1-5% good, 5-10% moderate, 10-20% low, and above 20% flags a discrepancy that likely needs investigation. These thresholds are general guidance, not a scientific standard — what counts as acceptable error varies significantly by field (a 2% error might be fine for a classroom experiment but unacceptable in precision manufacturing).

Constraints

The actual/expected value cannot be zero, since the formula divides by it; entering zero there returns an explicit division-by-zero error instead of a nonsensical result.