3. WHAT IS THE DIFFERENCE BETWEEN SYSTEMATIC & RANDOM ERROR?
a) SYSTEMATIC ERRORS/UNCERTANTIES (affect accuracy)
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Affect ALL measurements in the SAME way [i.e. measurements that are either consistently too high or too
low…due to systematic error(s)]
REPEATED measurements WILL NOT reveal this type of uncertainty regardless of the number of trials
performed.
Usually difficult to detect unless the expected/actual value is known.
Can sometimes be remedied/ameliorated/lessened.
Due to:
a) Miscalibrations / “faulty” or damaged equipment
Examples:
i)
A stopwatch running 2 seconds too slow.
*Repeated measurements will not reveal this uncertainty.
*Remedy = check clock against a more reliable one (If doubt the reliability of a measuring device try to check it
against a device that is more reliable.)
ii)
iii)
iv)
A miscalibrated pH metre will always display a reading that is either higher or lower than the actual value.
A thermometer reporting a temperature that is 2 degrees higher than the actual value.
A stretched ruler.
OR
b) Poor experimental technique (“human error”)
Examples:
i) Reading a meniscus incorrectly / parallax error?
ii) Reading scale incorrectly
iii) Unaccounted heat loss in a calorimetry experiment
*A note on “human error”:
Please don't refer to "human error." Examples of so-called human error include misreading a ruler, adding the wrong
reagent to a reaction mixture, mis-timing the reaction, miscalculations, or any kind of mistake. Scientists would never
report the results of an experiment affected by human error -instead, they repeat the experiment more carefully!
Points will be deducted from your lab report if you discuss "human error" instead of "experimental error."
b) RANDOM ERRORS/UNCERTANTIES (affect precision)
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Actual value may be higher OR lower than the value you record….in this manner, they are “random”...just as
likely to overestimate as to underestimate a measurement.
REPEATED measurements WILL reveal this type of uncertainty.
They are PREDICTABLE and UNAVOIDABLE.
Random errors can generally not be ameliorated.
Arise mostly from the INADEQUACIES or LIMITATIONS inherent to all measuring instruments.
The degree of random error can be QUANTIFIED.
The random error is equivalent to the uncertainty in measurement. This is usually given by the manufacturer of
the equipment and expressed as +/ - a certain value.
*If this information is not available, a good guideline is to estimate the uncertainty at HALF of the SMALLEST
division on a scale for an ANALOGUE device and the SMALLEST division for a DIGITAL device.
Note when the uncertainty is recorded, it should be to the same number of decimal places as the measured value.
For example a balance reading to 53.457g +/- 0.001 (↑ decimal places = ↑ sig. digs. = ↑ precision)
Example 1:
 Imagine trying to determine the period of a pendulum.
Best strategy = Make repeated measurements; calculate average (…the best average of the period is the average value)
*the mean is the best estimate of a measurement based on a set of measured values
*standard deviation = ?
e.g. Four measurements(in seconds) = 2.3, 2.4, 2.5, 2.4 ; average = 2.4
Range = 2.3 to 2.5
Example 2: multiple students take temperature of room (discuss both system and random error)
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Sources of random errors cannot always be identified. Possible sources:
a) observational e.g. reading burette, judging a colour change
b) environmental e.g. convection currents
*standard deviation and precision
When the final uncertainty arising from random errors is calculated, this can then be compared with the experimental error
as described above. If the experimental error is larger than the total uncertainty, then random error alone cannot explain
the discrepancy and systematic errors must be involved.
Almost all measurements are subject to both systematic and random uncertainties.
SUMMARY TABLE: SYSTEMATIC VS. RANDOM ERRORS
SYSTEMATIC
-affect results same way (either all too high or all too low)
-not revealed by repeated measurements
-can sometimes be eliminated
-often difficult to quantify (unless true value is known)
-cannot be treated statistically
RANDOM
- affect results randomly (some too high some too low)
-revealed by repeated measurements
-can never be eliminated
-can always be quantified
-can be treated statistically