Experimental Error
Errors are uncertainties in experimental data that can arise in numerous ways. There are four
major types of error: human error, random error, sampling error, and systematic (or procedural)
error. Below is a description of each type of error. In your error analysis you should focus on
systematic errors. You should avoid using vague terms such as: changed, affected, disrupted,
altered, or interfered. If the error increased the value of the data point, you must state that
explicitly. If the error decreased the value of the data point, you must state that explicitly. If it’s
not clear whether the error increased or decreased the value of the data point, you must state that
explicitly.
Human Error
Human error is simply another word for mistake, blunder, or screw-up. These are not errors in
the sense meant in this document. Examples include:
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Not setting up an experiment correctly
Misreading an instrument
Using the wrong chemical(s)
Not following directions as written
Spilling or general sloppiness
Bad calculations, doing math incorrectly, using the wrong formula
Students usually quote human error as a source of error, probably because they are easy to think
of. However, they are neither quantitative nor helpful in analyzing the data. Human errors are
NOT a source of experimental error; rather, they are “experimenter’s” error. Do not quote
human error as a source of experimental error in any lab report!
Random Error
Random errors are unavoidable variations that will either increase or decrease a given
measurement. Examples may include:
o Fluctuations in the laboratory balance (your sample may weight a few hundredths of a
gram higher or lower at any given time, depending on the quality of the balance and the
conditions in the room).
o Using a stopwatch to time a reaction (regardless of how careful you are, you will
sometimes stop the watch too soon and sometimes too late).
To minimize random errors, try to use consistent techniques and appropriate lab equipment when
performing an experiment. Since random errors are equally likely to be high as low, performing
several trials (and averaging the results) will also reduce their effect considerably. These types
of errors are usually unacceptable, but there may be exceptions if properly analyzed. See the
teacher if you are unsure whether your error works or not.
Sampling Error
Many scientific measurements are made on populations (or multiple samples). It is intuitively
understood that the more samples you have from a given population, the less the error is likely to
be. You should not be satisfied with two data points that are similar; it is slightly more
convincing to have three or four that are similar. While this may be relevant to the labs we do in
this class, it should generally not be used to explain your data anomalies.
Systematic (Procedural) Error
Systematic error is an error inherent in the experimental setup that causes the results to be
skewed in the same direction each time. These are usually due to a procedure that fails to control
for outside variables. For example:
o When testing enzyme activity at different temperatures, the procedure failed to acclimate
enzyme and substrate to the same temperature before putting them in contact with each
other (enzyme lab)
o The experiment occurred over several days so the freshness of the enzyme source
decreased – this could have caused less functional enzyme to be present (enzyme lab)
Since systematic errors always skew data in one direction, they cannot be eliminated by
averaging. A well-designed experiment will minimize these errors. It is not possible to give
detailed advice as to how systematic errors may be overcome. Each experiment must be
considered individually and only by a thorough understanding of the purpose of the experiment
and techniques used is this possible. These can usually be avoided by changing the way in which
the experiment was carried out (modifying the procedure, changing laboratory conditions, etc).
These are generally acceptable sources of errors for labs done in this class. Focus on these
types of errors as you interpret your data anomalies.
Adapted from:
http://www2.volstate.edu/tfarris/PHYS2110-2120/experimental_error.htm
http://www.rod.beavon.clara.net/err_orig.htm