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Math

Percent Error Calculator

Calculate the percent error between a measured and an accepted value.

Percent error = |Experimental − Theoretical| ÷ Theoretical × 100

Try an example

Percent error

2%

Signed difference-2

About this calculator

How This Percent Error Calculator Works

Enter an experimental (measured) value and a theoretical (accepted) value, and the calculator returns the percent error between them.

Worked Example

A measured value of 98 against a theoretical value of 100 gives a percent error of 2% — the measurement differs from the accepted value by 2% of that value.

The Formula

Percent error = |Experimental − Theoretical| ÷ Theoretical × 100. The absolute value means percent error is always reported as a positive magnitude — how far off the measurement is — while the signed difference (shown separately) indicates whether the measurement came in above or below the accepted value.

Common Uses

Percent error is a staple of science lab work — comparing an experimental measurement (like a calculated boiling point, or a physics experiment's result) against a known, accepted value. It's also used more broadly wherever an estimate or approximation needs to be checked against an actual or expected value, such as forecasting, engineering tolerances, or quality control.

Worked example

A measured value of 98 against a theoretical value of 100 gives a percent error of 2% — the measured value differs from the accepted value by 2%.

Frequently asked questions

Why does percent error use absolute value?

Percent error is typically reported as a magnitude — how far off the measurement is, regardless of whether it's too high or too low. The signed difference (shown separately) tells you the direction.

What counts as a 'good' percent error?

It depends entirely on context — a physics lab experiment might target under 5%, while some fields require much tighter tolerances. There's no universal threshold.

What if the theoretical value is zero?

Percent error is undefined when the theoretical value is zero, since it requires dividing by that value — a different error metric is needed in that case.

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