Percentage Error Formula:
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Percentage error is a measure of how inaccurate a measurement is, standardized to how large the measurement is. It is calculated as the absolute value of the difference between the true value and measured value divided by the true value, multiplied by 100%.
The calculator uses the percentage error formula:
Where:
Explanation: This formula quantifies the relative error between the true value and measured value as a percentage, providing a standardized way to compare measurement accuracy across different scales.
Details: Percentage error is crucial in scientific experiments, quality control, engineering, and research to evaluate the accuracy and reliability of measurements and experimental procedures.
Tips: Enter the true value and measured value in the same units. The true value must be greater than zero. The result shows the percentage error, with positive values indicating the measured value is lower than the true value, and negative values indicating the measured value is higher.
Q1: What is considered an acceptable percentage error?
A: Acceptable percentage error varies by field. In most scientific work, errors below 5% are generally acceptable, while in precise engineering, errors below 1% may be required.
Q2: What's the difference between percentage error and absolute error?
A: Absolute error is the simple difference between true and measured values, while percentage error expresses this difference relative to the true value as a percentage.
Q3: Can percentage error be negative?
A: Yes, negative percentage error indicates the measured value is greater than the true value, while positive error indicates the measured value is less than the true value.
Q4: Why is percentage error important in experiments?
A: It helps researchers evaluate the accuracy of their measurements, identify potential sources of error, and compare results across different experimental setups.
Q5: How can I reduce percentage error in measurements?
A: Use calibrated instruments, take multiple measurements, control environmental factors, follow proper measurement techniques, and use appropriate statistical analysis.