By David Dunning
Updated Mar 24, 2022

In scientific research, a constant error—also called a systematic error—creates a consistent deviation from the true value of a measurement. Unlike random errors that vary unpredictably, constant errors shift all readings in the same direction by a fixed amount.

Identifying Systematic Errors

Because systematic errors do not change when an experiment is repeated under the same conditions, they are often invisible to statistical analysis. The mean or median of the data will still carry the same bias, making the error difficult to detect without external reference.

How to Eliminate Constant Errors

Several strategies can reveal and correct systematic bias:

• Benchmarking: Compare your results with those obtained by a different method or instrument. A persistent offset indicates a systematic error.
• Calibration: Adjust your apparatus or procedure based on known reference standards. This ensures that any deviation is accounted for before measuring unknowns.
• Instrument Impact: Some devices can alter the quantity they measure (e.g., a voltmeter adding resistance to a low‑current circuit). Selecting appropriate equipment or measurement techniques can mitigate this effect.

Precision vs. Accuracy

Precision refers to the consistency of repeated measurements, while accuracy denotes closeness to the true value. A device with a mis‑graduated scale can produce highly precise but systematically inaccurate readings. Calibrating against a reference quantity removes this bias.

Zero Error

Zero error is a specific type of constant error where a measuring instrument does not read zero when it should. Examples include ammeters, voltmeters, stopwatches, and thermometers. Even if the instrument cannot be reset, the zero error can be corrected by adding or subtracting the offset from all subsequent readings.

By recognizing and correcting constant errors, researchers can ensure their data truly reflect the phenomena under investigation.