Experimental error is the difference between a measured value and its actual value. In other words, inaccuracies stop us from seeing a correct measurement.

Experimental error is prevalent and is, to some degree, inherent in every measurement. However, it is not usually seen as a ‘mistake’ in the traditional sense because a degree of error is perceived as part and parcel of the scientific process.

However, by accepting and understanding how experimental error can impact every scientific procedure, scientists can reduce inaccuracy and acquire results closer to the truth.

Here are why this might occur in an experiment, and these can be divided into subcategories: systematic errors, random errors, and blunders.

Systematic errors

These errors tend to be caused by the process, and their reason can usually be identified. Here are four significant types of systematic errors:

  1. Instrumental – When the tool you are measuring provides incorrect results, e.g., the fluid in a thermometer does not correctly represent the water temperature.
  2. Observational – When the measurement is consistently misread, e.g., a researcher records the water in a measuring cup from above, and the angle obscures the actual height of the water in the cup.
  3. Environmental – When the lab’s surroundings unintentionally influence the test results, e.g., the heat in the laboratory is always too high. It causes water to evaporate from a Petri dish at a higher-than-normal rate.
  4. Theoretical – When the model used to calculate data creates inaccurate results, e.g., when a formula for working out gravity’s influence on acceleration is used. Still, the procedure does not factor in the effect of air resistance on acceleration.

These errors are caused by unforeseeable and unknown factors surrounding the experiment. They often result in random fluctuations in data sets but can be identified or estimated through statistical analysis.

  1. Observational – When a researcher randomly takes an inaccurate reading, e.g., the researcher notes the volume of liquid to the minor division but occasionally determines the wrong number of milliliters.
  2. Environmental – When there are unforeseeable conditions surrounding the experiment, e.g., it’s a very wet day, affecting the humidity in the lab where an investigation with organic materials is being conducted.


These mistakes happen so infrequently that they are not considered random errors. However, it will usually be pretty evident in a data set because it will appear as a distinct anomaly.

  1. A Blunder – An outright mistake, e.g., a scientist not sealing the lid of a container properly and allowing gas to escape.
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