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Applied Example of Random and systematic errors in titrimetric analysis:. The example of the students’ titrimetric experiments showed clearly that random and systematic errors can occur independently of one another, and thus presumably
Applied Example of Random and systematic errors intitrimetric analysis:
The example of the students’ titrimetric experiments showed clearly that random
and systematic errors can occur independently of one another, and thus presumably
arise at different stages of the experiment. A complete titrimetric analysis in
aqueous solution with a colorimetric indicator can be regarded as having the following
Making up a standard solution of one of the reactants. This involves
a weighing bottle or similar vessel containing some solid material,
the solid material to a standard flask and weighing the bottle again to obtain by subtraction the weight of solid transferred (weighing by difference), and
the flask up to the mark with water.
an aliquot of the standard material to a titration flask with the aid of a pipette. This involves both filling and draining the pipette properly.
the liquid in the flask with a solution of the other reactant, added from a burette. This involves
(i) filling the burette and allowing the liquid in it to drain
until the meniscus is at a constant level,
(ii) adding a few drops of indicator solution
to the titration flask,
(iii) reading the initial burette volume,
(iv) adding liquid
to the titration flask from the burette a little at a time until, using a colour change, the end-point is judged to have been reached, and
(v) measuring the final level of liquid in the burette (e.p.).
Weighing procedures are normally associated with very small random errors. In
routine laboratory work a ‘four-place’ balance is commonly used, and the random
error involved should not be greater than ca. 0.0002 g.
If the quantity being weighed is normally ca. 1 g or more, it is evident that the random error, expressed as a percentage of the weight involved, is not more than 0.02%.
A good standardmaterial for volumetric analysis should (amongst other characteristics) have as high a formula weight as possible, in order to minimize these random weighing errors
when a solution of a given molarity is being made up. In some analyses ‘microbalances’
are used to weigh quantities of a few milligrams – but the errors involved are
likely to be only a few micrograms.
Systematic errors in weighings can be appreciable, and have a number of well
(i)These include adsorption of moisture on the surface of the weighing vessel;
(ii)failure to allow heated vessels to cool to the same temperature as
the balance before weighing;
(iii)corroded or dust-contaminated weights; and
For the most accurate work, weights must be calibrated against standards furnished by standards authorities .
Some simple experimental precautions can be taken to minimize these
systematic weighing errors. Weighing by difference cancels systematic
errors arising from (for example) the moisture and other contaminants on the
surface of the bottle . If such precautions are taken, the errors
in the weighing steps will be small, and it is probable that in most volumetric experiments
weighing errors will be negligible compared with the errors arising from the
use of volumetric equipment. Indeed, gravimetric methods are generally used for
the calibration of an item of volumetric glassware, by weighing (in standard conditions)
the water that it contains or delivers, and standards for top-quality calibration
experiments are made up by using weighings rather than volume
A- Random error:
Involumetric steps random errors arise in the use of volumetric glassware.
The error in reading a burette graduated in 0.1 ml divisions
is ca. 0.01–0.02 ml. Each titration involves two such readings .
If the titration volume is ca. 25 ml,
the percentage error is again very small. The experimental conditions should be
arranged so that the volume of titrant is not too small (say not less than 10 ml),
otherwise the errors will become appreciable.
(This precaution is analogous to choosing a standard compound of high formula weight to minimize the weighing error.) Even though a volumetric analysis involves several steps, in each of which a piece of volumetric glassware is used, it is apparent that the random errors should
be small if the experiments are performed with care. In practice a good volumetric
analysis should have a relative standard deviation of not more than
about 0.1%. Until recently such precision was not normally attainable in instrumental
analysis methods, and it is still not common.
an analytical laboratory may easily be several degrees different from this, and many experiments, for example in biochemical analysis, are carried out in ‘cold rooms’ at ca. 4°C. The temperature affects both the volume of the glassware and the density of liquids.
perhaps larger than the random errors in a typical titrimetric analysis. For example, in the titration of 0.1 M hydrochloric acid with 0.1 M sodium hydroxide, we expect the end-point to correspond to a pH of 7.
In practice, however, we estimate this end-point by the use of an indicator such
as methyl orange. Separate experiments show that this substance changes colour
over the pH range ca. 3–4. If, therefore, the titration is performed by adding alkali
to acid, the indicator will yield an apparent end-point when the pH is ca. 3.5, i.e.
just before the true end-point. The systematic error involved here is likely to be as
much as 0.2%.
Conversely, if the titration is performed by adding acid to alkali, the
end-point indicated by the methyl orange will actually be a little beyond the true
end-point. In either case the error can be evaluated and corrected by performing a
blank experiment, i.e. by determining how much alkali or acid is required to produce
the indicator colour change in the absence of the acid (alkali).
In any analytical procedure, classical or instrumental, it should be possible to consider and estimate the sources of random and systematic error arising at each separate stage of the experiment, as outlined above for titrimetric methods. It is very desirable for the analyst to do this, in order to avoid major sources of error by careful experimental design .
the data are arranged in ascending or descending order
Thus in ascending : 0.1019 , 0.1021, 0.1023, 0.1025
In descending: 0.1025. 0.1023, 0.1021, 0.1019
Since data are even we take the mean of the two middle value