Error Analysis
All scientific investigations have errors to some degree. An error is the difference between a
measurement and the true value being measured. Please note, errors are not mistakes due to
experimenter carelessness, sloppiness or being rushed.
An appreciation of error should be apparent at all stages of an investigation.
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In the planning stages, the limitations of the time and the materials should be assessed,
and the potential sources of error should be controlled.
In the data collection and processing stages, the degree of accuracy of a measuring device
should be stated.
In the evaluation of the investigation, the sources of error should be discussed, along with
the possible ways of avoiding them.
In general, errors can be classified as:
Systematic Errors: faults or flaws in the investigation design or procedure that shift all
measurements in a systematic way so that in the course of repeated measurements the
measurement value is constantly displaced in the same way. Systematic errors can be
eliminated with careful experimental design and techniques.
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Error in measurement instrument use and calibration: for example, if an electronic
scale reads 0.05 g too high for all mass measurements because it was improperly tared.
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Faulty measurement equipment: for example, if a tape measure has been stretched out
so everything you measured with it is larger than reality.
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Faulty use of measurement equipment: for example, if a researcher consistently reads
a graduated cylinder from above (as opposed to directly looking at the meniscus).
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Uncertainty of a measurement tool: all measurements have uncertainties and are only
as accurate as the tool being used to make the measurement. For general purposes,
the accuracy of a measurement device is one half of the smallest measurement possible
with the device.
Random Errors: errors caused by unknown and unpredictable changes in a
measurement, either due to measuring instruments or environmental conditions. You can't
eliminate random errors. You can reduce the effect of random errors by taking multiple
measurements and increasing sample sizes.
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Variation in measurement readings: for example, if one person reads 27.5 degrees and
another person reads 27.8 degrees when taking the temperature of the same
solution. This error can be minimized by taking more data and averaging over a large
number of observations.
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Sample size: a small sample size is going to increase the uncertainty of the conclusions
being drawn.
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Background noise: noise is extraneous disturbances that are unpredictable or random
and cannot be completely accounted for. For example, when taking an ECG
measurement, the presence of a cell phone near the monitoring device might affect the
measurements.
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Intrinsic variability: biological material is notably variable from subject to subject or
within the same subject over the course of time. For example, the solute concentration of
potato tissue may be calculated by soaking pieces of tissue in a range of concentrations of
sucrose solutions. However, different pieces of tissue will vary in their water potential
especially if they have been taken from different potatoes.
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Sensitivity limits: an instrument may not be able to respond or indicate a change in a
quantity that is too small or the observer may not notice the change. For example, the
resolution limitations of a microscope may limit an observer from noticing changes in a
cell structure.
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The act of measuring: when a measurement is taken this can affect the environment of
the experiment. For example when a cold thermometer is put in a test tube of warm
water, the water will be cooled by the presence of the thermometer. Or, an animal being
observed changes its behavior because of the presence of the researcher in its habitat.
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Experimenter fatigue: when people have to make a large number of tedious
measurements, their concentration spans vary. Automated measuring, for example
through the use of a data logger system, can help reduce the likelihood of this type of
error. Alternatively, the experimenter can take a break occasionally.
Once you have identified the sources of error, you must explain how they affected your results.
Did they make your experimental values increase or decrease? Why?