This function allows you to calculate the probability that a result is more or less far from the real value thus, at a distance of 1 standard deviation above or below, we will have 68.2% of the results at a distance of 2 standard deviations, 95.4% and at a distance of 3 standard deviations, 99.7%. The first step is to focus on the random error that, under the usual measurement conditions, is represented by a normal distribution function of the results (the so-called Gaussian Bell). However, the concept ‘expected value’ needs some additional clarification, since the control measure itself is subject to errors. If we obtain the expected value for the control sample, then it is assumed that the test samples are giving correct results (which is called series validation) otherwise (the control does not give the expected value), we would reject the series since the results would be compromised by an indeterminate error. The usual way of evaluating the quality of a series of measures is the use of control material that is, a sample whose composition is known for the parameters that we want to control and that we include along with other unknown samples. It is the use of these protocols that ultimately determines the reliability of the result. Quality protocols are the tools used to determine both the intensity of a random error (and determine when the established tolerance limits are exceeded) and to identify the existence of systematic errors that must be corrected. Those can be corrected by different procedures depending on the nature of the error (for example, by recalibrating the method). Other errors are systematic deviations caused by our measurement process. These types of errors cannot be eliminated, but they can be minimized. They are the consequence of the random dispersion around a mean value (the real value) and will present positive and negative differences with respect to it represented by the statistical parameter called standard deviation. Some of these errors are random and therefore unavoidable.

We call “errors” in an analysis to the difference between the value obtained for a sample and the actual value of the analyte in it.

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