Transcript Limitations of Analytical Methods

Limitations of Analytical
Methods

The function of the analyst is to obtain
a result as near to the true value as
possible by the correct application of
the analytical procedure employed.
Limitations of Analytical
Methods

The level of confidence in the results
will be very small unless there is a
knowledge of the accuracy and
precision of the method used as well as
being aware of the sources of error in
the measurement.
Data Handling

Accuracy and Precision

Statistics


Errors
Calibration Curves
Data Handling

Accuracy
 The
accuracy of a determination may be
defined as the concordance between it
and the true or most probable value.
Data Handling

Accuracy: Two possible ways of determining
the accuracy.
 Absolute
Method: Using a synthetic sample
containing known amounts of the constituents to
be determined.
 Comparative
Method: Using a standard sample of
the material in question.
Data Handling

Precision
 Precision
may be defined as the
concordance or reproducibility of a series
of measurements of the same quantity.
Data Handling

Precision
 This
definition can be further refined to take
account the timing of the experiment.
 Thus
there is a distinction between a series of
measurements made by one analyst on one day;
REPEATABILTY, and measurements made by
a number of analysts over several days;
REPRODUCIBILTY.
Data Handling

Precision
 Precision
always accompanies accuracy,
but a high degree of precision does not
imply accuracy.
Data Handling

Inaccurate and Imprecise
Data Handling

Accurate but Imprecise
Data Handling

Accurate and Precise
Data Handling

Inaccurate but Precise
Data Handling

Statistics
 The
true or absolute value of a quantity cannot
be established experimentally, so that the
observed value must be compared with the
most probable value.
 Statistics
provide a means of quantifying the
precision of a set of measurements.
Data Handling

Mean
 It
is found that the results of a
series of determinations will vary
slightly.
 The average value is accepted as
the most probable.
x
x=
n
Data Handling

Estimates of Precision
 Standard
Deviation
 Variance
 Relative
Standard Deviation
 Coefficient of Variation
Data Handling

Standard Deviation
 Defined
as the square root of the
sum of the squares of the deviation
from the mean.
Data Handling

Standard Deviation
s=
 ( x - x)2
n-1
Data Handling

Standard Deviation
s=
 ( x - x)2
n
Data Handling

Variance
 Is
the square of the standard
deviation.
2

(
x
x)
s2 =
n-1
Data Handling

Relative Standard Deviation
 A further
measure of precision is
known as the Relative Standard
Deviation (R.S.D.).
R.S.D. = s / x
Data Handling

Coefficient of Variation
 This
measure is often expressed as
a percentage as the coefficient of
variation (C.V.)
R.S.D. = 100s / x