Difference Between Systematic Error and Random Error

Edited by Diffzy | Updated on: April 30, 2023


Difference Between Systematic Error and Random Error

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Perfection is considered a myth. If the world were perfect, there would be no accidents. There would be no war, only serene peace. The sun would shine its brightest every day and the moon would glow flawlessly at night. Flowers would bloom gloriously in spring and birds would sing their melodies in joy. Horses would gallop in synchrony and puppies would all remain cute puppies forever. More importantly, there would be scope for mistakes or anything extra-ordinary.

Also, in a perfect world, people would all be clones of one another quite like the stormtroopers in the famous Star Wars franchise. Stormtroopers are known to follow orders without question and only do what is commanded of them. Their legendarily horrible aim could be an error in the programming and not their fault. Such unintentional blunders often wreak havoc in the Star Wars franchise, which, it should be noted is not an example of a perfect world. Albeit fictional, it is imperfect. It is the imperfections that make it so relatable and aspiring.

Systematic Error vs. Random Error

Imperfections constitute errors. Since our world is imperfect, we inhabit errors. Errors in nature are arbitrary and unprecedented. However, while experimenting, such errors are actively looked for and preferred to be prevented. The reason they are prevented is so that the results obtained have a higher relevance. In an experiment, the errors to be looked for are systematic and random. These are types of measurement errors, which are errors made in data observation. The systematic errors are those errors that are consistent and proportional while the random errors are due to chance. Let us see how these errors are further different from each other.

Differences Between Systematic Error and Random Error in a Tabular Form

Definition A systematic error is a consistent or a proportional difference in the observed value and the true value in an experiment. A random error is a chance difference between the observed value and the true value in an experiment.
Repetition A systematic error is repetitive. A random error is not repetitive.
Cause The cause of a systematic error is most likely a fault in the types of equipment being used in the experiment. The causes of random errors are most likely unpredictable variations, individual differences in the participants or changes in the environment.
Reduction Systematic errors can be reduced by repairing or replacing faulty equipment. Random errors can be reduced by conducting repetitions of the experiment to increase the number of observations.
Types There are two types of systematic errors – offset errors and scale factor errors. There are no types of random errors.
Predictability Systematic errors are predictable and can be repeated. Random errors are unpredictable.
The magnitude of error The magnitude of errors in systematic errors is constant. The magnitude of errors in random errors can vary.

What is a Systematic Error?

A systematic error is an error that is due to experimental equipment that is imprecise. It is a consistent error that is often a proportional difference between the observed and the true value in an experiment. It means that the measurement of the same thing will constantly change in predictable ways – the measurement will differ from the true value in the same direction by the same amount. It is also known as systematic bias since the incorrect observations obtained in standardized ways hide the true values and give false conclusions.

In the field of research, systematic errors are considered a big deal. They affect the accuracy of the result – how close or far the observed and the true values are from each other. In such a case, the measurements get skewed away from the true value and lead to false conclusions. It also could lead to false-positive and false-negative errors.

There are two types of systematic errors:

  • Offset error: This systematic error occurs when the scale is not calibrated to the zero-point. For this reason, it is also known as an additive error or a zero-setting error.

For example: In an experiment, if the experimenter measures the girth of a tree and they wrongly read 2 as the zero-point, every observation will be increased by 2 units.

  • Scale factor error: This systematic error occurs when there is a consistent and proportional difference between the true value and an observed value. Here, the scale is at fault. This error is also known as a correlational error or a multiplier error.

For example: In an experiment, if the weighing scale is faulty and adds 10% to the weight, the measurement of 10kgs will be falsely weighed as 11kgs.

Sources of Systematic Error:

There can be multiple sources of systematic errors. They could be errors in the research material or even in the analysis techniques.

  • Response Bias: This is an example of errors in the research material. Consider research material like a questionnaire. The responders might be biased to answer in a certain way due to the ‘social desirability’ bias, which is that they would prefer to adhere to societal norms rather than answer how they feel.
  • Experimenter Drift: This error occurs when the experimenter is exhausted after prolonged periods of data collection and coding. The experiment might get boring or less motivating after a time. They might fail to use the standardized methods while experimenting.
  • Sampling Bias: This type of error occurs when only a certain type of people in the population is included in the sample. Such an error leads to the results being less generalizable. For example: In an experiment, about sleep patterns, if only athletes are included, the results cannot be generalized to the public.

Systematic errors can be reduced. Following are the methods that can reduce systematic errors in an experiment:

  • Triangulation: This method includes involving various other techniques to experiment. It ensures that the experimenter is not relying on just one observation method.

For example: In an experiment assessing anxiety levels, a survey response, physiological readings and brain scans can be used. The results from all three methods can be assessed and checked if they converge.

  • Regular calibration: All the instruments used for measuring must be regularly calibrated i.e. checking if the values correspond to the standard scale of measurement. For qualitative studies, the researchers must calibrate their codes by measuring them against the standard protocols to avoid experimenter drift.
  • Randomization: This is the method of randomizing the sample so that it does not significantly differ from the population. It improves the generalizability of the results.
  • Masking: This method keeps the participants in the dark or masks them from the condition. It has been found that the participants’ behaviour can be influenced by the expectations of the researcher and thus, masking is advised to avoid bias.

What is a Random Error?

A random error is an error that isn’t necessarily a mistake. It is an error that fluctuates due to the unpredictability that occurs in the experiment. Thus, the error or difference between the true and the observed value is caused by chance.

For example, a scientist measuring the length of a worm might use a scale with one end at the zero point. But the worm itself might move and the position of the head might change leading to an inaccurate measure of its length.

Random errors, essentially, are unavoidable. They are a natural part of the measurement of data. There is bound to be variability in the measurements despite multiple recordings because of the variations in the environment or scale or even the researcher’s interpretations.

Random errors are variations between the measurements of the same thing. Repeating the experiment multiple times will give multiple values that cluster around the true value. Hence repeated measurements and averaging them can lead the researcher closer to the true value. This is the reason a random error is not considered as significant as a systematic error. However, it could affect the precision of the results in a small sample size.

Random error is often called ‘noise’ since it distorts the true value from being observed with clarity. The researcher must try to keep the random error value low to receive a precise result.

Following are the sources of random error in an experiment:

  • Natural variations: Variations in the environmental conditions cause these errors. For example: While testing for intelligence, the participants cannot all be scheduled for the test randomly. Evidence suggests that some people are better performers earlier in the day while others are better in the evening. Thus, the results would not reflect the true intelligence of the participants.
  • Imprecise instruments: Employing imprecise instruments causes the error. For example: Consider the tape measure with values in centimetres and half a centimetre; not millimetres. If such a tape is used and the true measure is, say, 5.7cm but since the tape does not have millimetres, the researcher would round up the measure as 6 cm or 5.5 cm, which, in essence, would be inaccurate.
  • Individual differences: These errors occur because of differences between the participants or units. For example: In an experiment studying pain, if a participant is asked to self-administer a shock and rate the pain value, the answers would vary depending on the experience of pain. Pain is a subjective experience. Thus, some participants might overstate their pain level while others might understate it.

The following methods can reduce random errors:

  • Repeated measurements:

Random errors can be reduced by experimenting multiple times to obtain multiple observations. The observations can then be averaged and the value obtained would be closer to the true value.

  • Large sample size:

A large sample size seems to have lesser random error since the differences cancel each other out more efficiently. It has also been found that data collection from a large sample size improves the precision of the results.

  • Control of variables:

In controlled experiments, like most laboratory experiments, the extraneous variables must be controlled. Extraneous variables are those variables that are not part of the study but are present nevertheless. For example: while testing for memory, extraneous variables could be – test anxiety and the stress level. 

Differences Between Systematic Error and Random Error in Points

Following are the main differences between Systematic Errors and Random Errors:

  1. A systematic error is an error that is due to a consistent difference in the observed and true value in an experiment whereas a random error is a chance difference in the observed and true value.
  2. A systematic error is caused due to faulty equipment in the experiment while a random error is caused due to unpredictable variations while experimenting.
  3. A systematic error can be controlled or reduced by repairing or replacing faulty equipment. A random error can be reduced by running repetitions of the same experiment to increase the number of observations.
  4. An experimenter can avoid a systematic error by improving the controls of the experiment but a random error is most times unavoidable.
  5. A systematic error is mainly of 2 types – an offset error and a scale factor error. Random errors have no types.
  6. Since the error is constant in a systematic error, they are predictable. Random errors are unpredictable.
  7. The magnitude of error in a systematic error is always constant while in the case of random error, the magnitude of error varies.
  8. In the research field, systematic errors affect accuracy while random errors affect precision.


Systematic errors and random errors, thus, are experimental errors in the field of research that can affect the results quite significantly. Systematic errors, though, are considered more detrimental than random errors. They occur when there is a persistent difference between the observed and true value and are most times caused by faulty equipment. Systematic errors affect the accuracy of the results and can be avoided by employing various research techniques.

Random errors, on the other hand, are unavoidable. They are errors caused by chance, in that they could be a result of variation in nature, imprecise instrument or differences in the participants or units of the experiment. These errors affect the precision rather than the accuracy of the results. These errors can be reduced by performing the experiment multiple times and taking an average of the result. Such a result would be found to be closer to the true value.

While our world is imperfect, there is little we can do to change it. Errors, ultimately, cannot be avoided but minimized. In the field of research, the lesser the fluctuations, the better for observation and conclusions. At least in that field, we can get as close to perfection as possible.


  1. https://www.formpl.us/blog/systematic-research-errors
  2. https://www.scribbr.com/methodology/random-vs-systematic-error/
  3. https://sciencing.com/difference-between-systematic-random-errors-8254711.html



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"Difference Between Systematic Error and Random Error." Diffzy.com, 2024. Mon. 10 Jun. 2024. <https://www.diffzy.com/article/difference-between-systematic-error-and-random-error-223>.

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