Percent Error Calculator

Percent error is a measure of the accuracy of a measurement or calculation compared to the true or accepted value. It's calculated as the absolute difference between the observed value and the true value, divided by the true value, multiplied by 100.

The formula for percent error is: ((Observed Value - True Value) ÷ True Value) × 100. This gives you the percentage difference between your measurement and the accepted value.

A positive percent error means your observed value is higher than the true value (overestimation). For example, if you measured 105 when the true value is 100, you have a 5% positive error.

A negative percent error means your observed value is lower than the true value (underestimation). For example, if you measured 95 when the true value is 100, you have a -5% error.

Percent error is commonly used in scientific experiments, quality control, engineering measurements, and any situation where you need to assess the accuracy of measurements or calculations compared to known standards.

A good percent error depends on the context. In most scientific applications, errors under 5% are considered acceptable, while errors under 1% are excellent. However, this varies by field and application requirements.

Percent error compares a measured value to a known true value, while percent difference compares two experimental values to each other. Percent error is used when you have a reference standard, while percent difference is used when comparing two measurements.

Yes, percent error can exceed 100%. This happens when the observed value is more than double the true value. For example, if the true value is 50 and you measure 150, the percent error is 200%.

In experiments, percent error helps you evaluate the accuracy of your measurements. Lower percent errors indicate more accurate measurements. Consider factors like instrument precision, human error, and systematic biases when interpreting results.

Common sources include measurement instrument limitations, human reading errors, environmental factors, systematic biases, and random variations. Understanding these sources helps improve measurement accuracy.