Random Error vs. Systematic Error

Two Types of Experimental Error

Laboratory glassware in lab, Measuring flasks and cylinders containing chemicals during experiment
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No matter how careful you are, there is always error in a measurement. Error is not a "mistake"—it's part of the measuring process. In science, measurement error is called experimental error or observational error.

There are two broad classes of observational errors: random error and systematic error. Random error varies unpredictably from one measurement to another, while systematic error has the same value or proportion for every measurement.

Key Takeaways

  • Random error causes one measurement to differ slightly from the next. It comes from unpredictable changes during an experiment.
  • Systematic error always affects measurements the same amount or by the same proportion, provided that a reading is taken the same way each time. It is predictable.
  • Random errors cannot be eliminated from an experiment, but most systematic errors can be reduced.

Random Error Example and Causes

If you take multiple measurements, the values cluster around the true value. Thus, random error primarily affects precision. Typically, random error affects the last significant digit of a measurement.

The main reasons for random error are limitations of instruments, environmental factors, and slight variations in procedure. For example:

  • When weighing yourself on a scale, you position yourself slightly differently each time.
  • When taking a volume reading in a flask, you may read the value from a different angle each time.
  • Measuring the mass of a sample on an analytical balance may produce different values as air currents affect the balance or as water enters and leaves the specimen.
  • Measuring your height is affected by minor posture changes.
  • Measuring wind velocity depends on the height and time at which a measurement is taken. Multiple readings must be taken and averaged because gusts and changes in direction affect the value.
  • Readings must be estimated when they fall between marks on a scale or when the thickness of a measurement marking is taken into account.

Because random error always occurs and cannot be predicted, it's important to take multiple data points and average them to get a sense of the amount of variation and estimate the true value.

Systematic Error Example and Causes

Systematic error is predictable and either constant or else proportional to the measurement. Systematic errors primarily influence a measurement's accuracy.

Typical causes of systematic error include observational error, imperfect instrument calibration, and environmental interference. For example:

  • Forgetting to tare or zero a balance produces mass measurements that are always "off" by the same amount. An error caused by not setting an instrument to zero prior to its used is called an offset error.
  • Not reading the meniscus at eye level for a volume measurement will always result in an inaccurate reading. The value will be consistently low or high, depending on whether the reading is taken from above or below the mark.
  • Measuring length with a metal ruler will give a different result at a cold temperature than at a hot temperature, due to thermal expansion of the material.
  • An improperly calibrated thermometer may give accurate readings within a certain temperature range, but become inaccurate at higher or lower temperatures.
  • Measured distance is different using a new cloth measuring tape versus an older, stretched one. Proportional errors of this type are called scale factor errors.
  • Drift occurs when successive readings become consistently lower or higher over time. Electronic equipment tends to be susceptible to drift. Many other instruments are affected by (usually positive) drift, as the device warms up.

    Once its cause is identified, systematic error may be reduced to an extent. Systematic error can be minimized by routinely calibrating equipment, using controls in experiments, warming up instruments prior to taking readings, and comparing values against standards.

    While random errors can be minimized by increasing sample size and averaging data, it's harder to compensate for systematic error. The best way to avoid systematic error is to be familiar with the limitations of instruments and experienced with their correct use.

    Key Takeaways: Random Error vs. Systematic Error

    • The two main types of measurement error are random error and systematic error.
    • Random error causes one measurement to differ slightly from the next. It comes from unpredictable changes during an experiment.
    • Systematic error always affects measurements the same amount or by the same proportion, provided that a reading is taken the same way each time. It is predictable.
    • Random errors cannot be eliminated from an experiment, but most systematic errors may be reduced.

    Sources

    • Bland, J. Martin, and Douglas G. Altman (1996). "Statistics Notes: Measurement Error." BMJ 313.7059: 744.
    • Cochran, W. G. (1968). "Errors of Measurement in Statistics". Technometrics. Taylor & Francis, Ltd. on behalf of American Statistical Association and American Society for Quality. 10: 637–666. doi:10.2307/1267450
    • Dodge, Y. (2003). The Oxford Dictionary of Statistical Terms. OUP. ISBN 0-19-920613-9.
    • Taylor, J. R. (1999). An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements. University Science Books. p. 94. ISBN 0-935702-75-X.