Comparison of results - parametric tests

The comparison of the standard deviations Si and S2 of two different sets of results is known as a test of the variance equality. In this test, the F factor, which is the ratio of the two variances is calculated in order that F 1  

The null hypothesis (statistical terminology), states that if there are no significant differences in the variances, then the ratio must be close to 1. Reference should therefore be made to the Fisher-Snedecor values of F, established for a variable number of observations (Table 22.3). If the calculated value for F exceeds that found in the table, the means are considered to be significantly different. Since the variability is greater than si, then the second series of measurements is therefore the more precise one. 

The adaptation of this test for robust statistics (non-parametric tests) requires simply to calculate the ratio F = R1/R2 from the two distributions (the difference between the two extreme measurements) of the two series under comparison. 

Number of measures (denominator) Number of measures (numerator of the fraction F)  

The two expressions 22.19 and 22.20 are useful for estimating the smallest concentration of an analyte that can be detected, but not quantified with a pre-selected level of confidence. One of the two means (T2 for example) is considered to be the result of a set of measurements made on the analytical blank. It will be noted as Xj, with a standard deviation of If = 1, then expression 22.19 simplifies to leading to expression 22.21 (using a t value extracted from Table 22.2 for 

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