Experimental error

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Experimental error is the difference between the values we receive when conducting the experiment. It is impossible to analyze the results of the experiment if we do not have certain assumptions about the error of the experiment, it is very important[1]. According to D.H. Stamatis: "Therefore, when we do an experiment, we assume that the response measure Y, is a function of a) parameters related to the experimental design and b) experimental error"[2]. An experiment using a smaller experimental error is usually stronger than an experiment using a more experimental error[3]. Sources of experimental error[4]:

  • sampling
  • assignment
  • conditions
  • measurement

Types of experimental errors

Experimental errors are divided into constant or systematic error and random error. The basis of experimental reasoning is understanding this type of error and learning how to deal with it[5].

Systematic or constant error it is characterized by the fact that the experimental conditions are the same each time the experiment is repeated. Constant error is e.g. an error resulting from the time of day. The effect of this error is masking by distorting the results[6].

A random error favoring one experimental condition or sometimes an accident is called a random error. If the error has constant and random components then it is an error from any source. The effect of this error is obscuring the results, it does not distort them in any[7].

Conduct in case of experimental error

Experimental error affects the experiment, so it should be repeated to draw the appropriate conclusions. Replication is any full repetition of an experiment. The best procedure is to estimate the possible experimental error before the experiment based on your previous experience. Then determine the number of repetitions and perform all parts of the experiment in random order. In order for the conclusions to be more reliable, the experimental technique should be improved[8]. Stamatis D.H. wrote: "Because a few replications of a refined technique can achieve the same reliability as many replications of a coarse technique, the choice of method in particular investigation may be made on the basis of cost"[9].

Examples of Experimental error

  • Measurement Error: Measurement errors occur due to imperfections in instruments used to measure physical properties. For example, if a thermometer is not calibrated correctly, it may give a reading that is too low or too high.
  • Human Error: Human error occurs when an experimenter's judgment or technique is incorrect or incorrect calculations are made. For example, a researcher may not follow the instructions of the experiment properly or make mistakes in measuring or recording the results.
  • Sampling Error: Sampling error occurs when a sample size is too small or does not represent the population accurately. For example, if a researcher surveys 1000 people but doesn't account for regional differences, the results may not be representative of the population as a whole.
  • Instrumental Error: Instrumental error occurs when the instrument used to measure a property is not precise or accurate. For example, if a thermometer has a low accuracy rating, its readings may not be very accurate.
  • Systematic Errors: Systematic errors are errors that are consistent and occur due to an underlying cause. For example, if a thermometer is calibrated incorrectly, it may always read too low or too high.

Advantages of Experimental error

Experimental error provides a number of advantages for experiments and research. These include:

  • Improved accuracy and reliability of results. Experimental error provides a measure of the accuracy of the data collected and can be used to determine the range of expected results. This helps to increase the reliability of the results, giving researchers and scientists more confidence in their conclusions.
  • More valid conclusions. By taking into account experimental error, researchers can draw more valid conclusions from the data. This helps to ensure that the results from the experiment are not simply due to chance or errors.
  • Improved understanding of the experiment. Understanding the error of the experiment can help to explain any discrepancies in the data, as well as provide insight into potential sources of error. This can help to improve the design of the experiment and the results.

Limitations of Experimental error

Experimental error has several limitations which make it difficult to accurately measure results. These include:

  • Systematic errors which are errors caused by the design or set up of the experiment, such as an incorrect calibration or an inaccurate measurement tool. Systematic errors can be difficult to identify and correct.
  • Random errors which are errors that are caused by unpredictable factors, such as human error or environmental factors. Random errors can be difficult to eliminate.
  • Instrumental errors which are errors caused by the instrument used to measure the experiment. These can include calibration errors, sensor drift, and other errors that can affect the accuracy of a measurement.
  • Sampling errors which are errors caused by the sample size or selection used for the experiment. If the sample size is too small or the selection is not representative of the entire population, this can lead to inaccurate results.
  • Model errors which are errors caused by the model used to interpret the results of the experiment. If the model is incorrect or incomplete, the results may be inaccurate.

Other approaches related to Experimental error

There are several additional approaches to understanding and interpreting experimental errors. These include:

  • Error Analysis: This approach involves analyzing the sources of errors, their magnitude and how they affect the results. This helps to determine how to reduce the errors and improve the accuracy of the results.
  • Error Estimation: This approach involves estimating the errors in the data by using statistical methods. This helps to determine the reliability of the results and understand the implications of the errors.
  • Error Compensation: This approach involves compensating for the errors in the data by using mathematical or statistical methods. This helps to reduce the errors and improve the accuracy of the results.
  • Error Correction: This approach involves correcting the errors in the data by using mathematical or statistical methods. This helps to reduce the errors and improve the accuracy of the results.

In summary, there are several approaches to understanding and interpreting experimental errors. These include error analysis, error estimation, error compensation, and error correction. These approaches help to reduce the errors and improve the accuracy of the results.


Experimental error — recommended articles
Adjusted mean — Statistical power — Random error — Test validity — Three-Way ANOVA — Correlational study — Measurement uncertainty — Lurking variable — Attribute control chart

References

Footnotes

  1. ↑ Stamatis D.H.(2002)
  2. ↑ Stamatis D.H.(2002)
  3. ↑ Stamatis D.H.(2002)
  4. ↑ Burns R.B.(2012)
  5. ↑ Burns R.B.(2012)
  6. ↑ Burns R.B.(2012)
  7. ↑ Burns R.B.(2012)
  8. ↑ Stamatis D.H.(2002)
  9. ↑ Stamatis D.H.(2002)

Author: Oliwia Kamińska

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