The chi-square statistic test illustrates the extent of the association between the measured values and the envisaged values (Puncher 2007). The incorporation of the chi-square test into the IMBA software enables it to approximate the bioassay intake function. The chi-square scheme entails a cluster of distributions influenced by degrees of freedom. The degree of freedom is arrived at by subtracting one from the total number of the measured data points. Consequently, the chi-square probability (which is also referred to as the P value) can be established from the data using the degrees of freedom of the measured values. One of the most significant uses of the chi-square test is hypothesis testing. The following equation illustrates how the chi parameter is computed.
In the above equation, μ’ signifies the average of the experimental data while xi denotes the actual value in the group of data (Knoll 2000).
Small values of chi-square are obtained when P is greater than 0.95. Such small values of P show that there is a good relationship between the calculated and predicted variations in the data. In contrast, high chi-square values are obtained when P is smaller than 0.05. Such high values are signs of inadequate fits between the measured and expected variations in the data.
