When to use percent error
Science classes use percent error to judge how close a trial is to a trusted reference—speed of sound tables, known sample masses, tabulated constants, or manufacturer specs. You always need an accepted number to compare against; without it, talk about absolute difference or relative spread between trials instead.
Formulas this tool applies
| Quantity | Expression |
|---|---|
| Absolute error | experimental value − accepted value |
| Unsigned % error | |experimental − accepted| ÷ |accepted| × 100% |
| Signed relative % | (experimental − accepted) ÷ accepted × 100% |
Do I use experimental or accepted in the numerator?
The numerator is always the gap between the two measured numbers; the accepted value lives in the denominator for the standard percent error definition so you express mismatch relative to the trusted benchmark.
What counts as an acceptable percent error?
That depends on instrumentation, sample purity, and course standards—there is no universal magic threshold. Compare against typical uncertainty for your device or ask your instructor for target tolerances.
How do I calculate percent error step by step?
Subtract the accepted benchmark from what you measured. For the nonnegative lab score, divide the absolute mismatch by the absolute accepted number (never divide by zero) and multiply by 100%.
Does percent error use absolute values?
The rubric most teachers ask for ignores sign so grading focuses on magnitude. Signing the quotient still matters when you diagnose bias, which is why the tool lists both interpretations.
What counts as experimental vs accepted value?
Experimental is literally what your procedure produced. Accepted is whichever trusted comparison your lab manual names— datasheet, CODATA constant, textbook model, calibration reference, etc.
What is systematic error versus random error?
Systematic error shifts readings the same direction every time unless you fix calibration. Random error makes individual trials wiggle yet tends to wash out once you replicate enough measurements and average thoughtfully.