Systematic errors are those that have a definite value which can be measured and accounted for, that is, they are errors that are possible to avoid, minimise or compensate for. Random errors result from the person, the equipment or the method operating outside its limitations. These errors may or may not be positively identified and so will not always have a definite measurable value. Random errors occur in a random manner. Systematic and random errors can be minimised by: calibration of apparatus and application of correction factors use of blanks, spiked samples, controls, certified reference materials, recovery tests and variations in sample size alternate or independent methods of analysis replicate determinations standard addition techniques internal standard techniques validation using the literature or prior work with similar samples Basic Numerical Methods for Reporting Measurements When any scientific measurement is carried out, it is not enough to simply carry out one analysis, and to report the result. There are too many sources of possible error that can arise, and therefore the analysis should be repeated a number of times to confirm that the measurement obtained is reliable. The result that is reported after several, acceptable, measurements of the same property is the average of the repeats or the mean. This is calculated from the sum of the answers divided by the number of values. For example, an analyst determines the vitamin C content of a sample and she obtains the following results: 24.39 mg/L, 24.18 mg/L, 24.27 mg/L. The reported value for this analysis would be the mean of these determinations, that is: ( 24.39 + 24.18 + 24.27 ) / 3 = 24.28 mg/L. The two attributes needed to accept these data are: accuracy (i.e. the result should be as close as possible to the actual value) precision (i.e. the replicate values should be close to each other). In this case, if the accuracy could not be estimated, the precision would be considered good and the analyst should feel confident with her work. When accuracy estimates are essential (e.g. nutritional labelling, medical analysis), then a number of validation methods exist. Routine validation methods which could be used are:
Systematic errors are those that have a definite value which can be measured and accounted for, that is, they are errors that are possible to avoid, minimise or compensate for. Random errors result from the person, the equipment or the method operating outside its limitations. These errors may or may not be positively identified and so will not always have a definite measurable value. Random errors occur in a random manner. Systematic and random errors can be minimised by: calibration of apparatus and application of correction factors use of blanks, spiked samples, controls, certified reference materials, recovery tests and variations in sample size alternate or independent methods of analysis replicate determinations standard addition techniques internal standard techniques validation using the literature or prior work with similar samples Basic Numerical Methods for Reporting Measurements When any scientific measurement is carried out, it is not enough to simply carry out one analysis, and to report the result. There are too many sources of possible error that can arise, and therefore the analysis should be repeated a number of times to confirm that the measurement obtained is reliable. The result that is reported after several, acceptable, measurements of the same property is the average of the repeats or the mean. This is calculated from the sum of the answers divided by the number of values. For example, an analyst determines the vitamin C content of a sample and she obtains the following results: 24.39 mg/L, 24.18 mg/L, 24.27 mg/L. The reported value for this analysis would be the mean of these determinations, that is: ( 24.39 + 24.18 + 24.27 ) / 3 = 24.28 mg/L. The two attributes needed to accept these data are: accuracy (i.e. the result should be as close as possible to the actual value) precision (i.e. the replicate values should be close to each other). In this case, if the accuracy could not be estimated, the precision would be considered good and the analyst should feel confident with her work. When accuracy estimates are essential (e.g. nutritional labelling, medical analysis), then a number of validation methods exist. Routine validation methods which could be used are: