Glossary
Topic 8 Foundation Engineering “A”
Peter,
glossary …………….
Accuracy:
When an experiment is repeated a sufficient number of times under identical
conditions the results may be plotted as a distribution. The difference between the
average of this distribution and the true value is the accuracy. Thus accuracy is
different from precision; see “Precision” in this glossary.
Precision:
When an experiment is repeated a sufficient number of times under identical
conditions the results may be plotted as a distribution. The spread or scatter of the
experimental values is the precision. It is also called the reproducibility or
repeatability. Thus precision is different from accuracy; see “Accuracy” in this
glossary.
Significant Figures:
Significant figures and significant digits are used interchangeably. The number of
significant figures is the number if digits in an experimental value that are
significant in terms of the precision of the measurement.
Rounded:
A calculator often produces an answer to many decimal places. Such a result
should be rounded to retain only the number of significant digits in accordance
with the rules of significant figures and the rules of significant arithmetic, see both
“Significant Figures” and “Significant Arithmetic” in this glossary. It is good practice
in a chain of calculations to hold intermediate answers to a higher precision and
then round once the final result has been found.
Significant Arithmetic:
Significant arithmetic refers to the multiplication, division, addition and subtraction
of experimentally determined values. There are rules to determine the number of
significant figures that should be applied to the result. However, the basic
requirement is that the result can be no more precise than the least precisely
known of the starting value.
Significant Multiplication:
The rule of significant multiplication is that if two experimental quantities are
multiplied together, the result of the calculation should be rounded to the number
of significant figures that applies to the least precisely known of the two starting
values. This is the same rule that applies to “Significant Division”, see this
glossary. What is important is the smaller number of significant figures of the two
starting values.
Significant Multiplication:
The rule of significant division is that if two experimental quantities are divided one
into the other, the result of the calculation should be rounded to the number of
significant figures that applies to the least precisely known of the two starting
values. This is the same rule that applies to “Significant Multiplication”, see this
glossary. What is important is the smaller number of significant figures of the two
starting values.
Significant Addition:
The rule of significant addition is that if two experimental quantities are added
together, the result of the calculation should be rounded to the position of the least
significant digit of the more imprecisely known of the two starting values. This is
the same rule that applies to “Significant Subtraction”, see this glossary. What is
important is the position of the least significant figure not the number.
Significant Subtraction:
The rule of significant subtraction is that if two experimental quantities are
subtracted one from the other, the result of the calculation should be rounded to
the position of the least significant digit of the more imprecisely known of the two
starting values. This is the same rule that applies to “Significant Addition”, see this
glossary. What is important is the position of the least significant figure not the
number.
Random Error:
Random errors are an unpredictable variation in either the measured instrument
reading from its true value, or a random variation of an observer’s interpretation of
the measurement from its true value. Random errors are evident when the
measured values vary randomly around the true value; all measurements contain
random errors
Systematic Errors:
Systematic errors are identified as an offset of the measured value from its true
value and are caused either by the instrument or the observer’s technique or
environmental interference. Systematic errors are evident when the measured
value varies randomly, but is always offset either on one side or the other from the
true value
Propagation Errors:
These are errors that arise because experimental measured values are entered
into a function to find some result and, as a result of the nature of the function,
errors propagate through to the final result. However, knowing the functional
relationship between the starting values mathematics may be used to predict the
propagation error in the final result.