Measured value90
True value100
Absolute error10
Percent error10%

The formula

error=VmVtVt×100\text{error} = \left| \dfrac{V_\text{m} - V_\text{t}}{V_\text{t}} \right| \times 100
Vₘ — measured (experimental) value
Vₜ — true (accepted) value
error — percent error

How it works

Percent error shows how far a measured value is from the true value, as a percentage. It is the standard way to report accuracy in a science experiment — a small percent error means your measurement was close to the accepted answer.

FAQ

Is percent error ever negative?

By convention it is reported as a positive number, using the absolute value, because it measures size of the gap not direction. Some courses keep the sign to show whether the estimate was high or low.

What counts as a good percent error?

It depends on the field, but in a school lab anything under about 5% is usually considered a good result. Precise instruments in research aim for far less.

Why divide by the true value instead of the measured value?

Dividing by the true (accepted) value gives a fixed, consistent reference point, since the true value does not change no matter what you measure. Dividing by the measured value instead would make the percentage shift depending on the size of your own error.

Is percent error the same as percent difference?

No — percent error compares a measurement against a known, accepted true value, while percent difference compares two measured values where neither is treated as correct. Percent difference is typically divided by the average of the two values rather than a single true value.

What is the difference between systematic and random error?

Systematic error is a consistent bias, such as a miscalibrated scale, that pushes every measurement the same way and can be corrected once identified. Random error varies unpredictably from trial to trial and is usually reduced by averaging repeated measurements.

Can the true value be negative or zero?

The formula works with a negative true value as long as you use its actual signed number, since the absolute value in the numerator still gives a positive result. If the true value is zero, percent error is undefined because you cannot divide by zero.

Should I average multiple measurements before finding percent error?

Yes — taking the mean of several trials and comparing that average to the true value gives a more reliable percent error than relying on a single reading. It also helps separate random scatter from a genuine systematic bias in your method.

About the percent error calculator

This calculator finds the percent error between a value you measured and the value that is actually correct. It is one of the first tools taught in science classes because every real measurement carries some error, and expressing that error as a percentage makes it easy to judge and compare. A thermometer, a scale or a stopwatch can all be checked this way against a known reference.

How to use it

Enter the measured value — the result you got from your experiment — and the true value, which is the accepted or expected answer. The calculator returns the percent error along with the raw difference between the two. For example, if you measured 90 but the true value is 100, the error is 10 units, which is a 10% error. The smaller the percentage, the more accurate your measurement was.

The formula

Percent error is the size of the difference divided by the true value, times 100: error=VmVtVt×100\text{error} = \left| \frac{V_\text{m} - V_\text{t}}{V_\text{t}} \right| \times 100, where VmV_\text{m} is the measured value and VtV_\text{t} is the true value. The vertical bars mean absolute value, so a measurement that is too high and one that is too low by the same amount give the same percent error. Dividing by the true value, not the measurement, keeps the comparison fair.

Where it is used

Students use percent error to write up physics and chemistry labs, comparing their results with textbook values. Engineers and technicians use it to calibrate instruments and check quality against a standard, while manufacturers use it to keep parts within tolerance. It also appears in forecasting and statistics, where a predicted value is compared with what actually happened to measure how good the model is.