A method for improved evaluation of an interaction between an analyte in a fluid sample and a ligand immobilized on a sensor surface of a biosensor, comprising the steps of - providing a first sensor surface, and providing a second sensor surface having a ligand immobilized thereon, - allowing a plurality of fluid samples to flow across said first and said second sensor surface wherein the fluid samples comprise a solvent at known concentration - measuring changes of refractive index at the first and second sensor surface during the flow of each fluid sample - determining a response value and a reference-subtracted response value for each fluid sample based on said changes and creating a data set comprising said values, each fluid sample forming a point in the data set - selecting a reliable set comprising at least five fluid samples from the data set, plotting the response value against the reference-subtracted response value for the reliable set and fitting a curve to them, the curve being used to test the remaining fluid samples from the data set to determine if any of them are outliers and should be removed from the set to increase the quality of the evaluation, said test comprising the steps a)-f) below, said steps being performed in alphabetical order for every other remaining fluid sample in the data set a) calculating leverages h i, b) labelling points with h i > h i cut as outliers, and if the number of outliers, n o, is less than n o max = min(n-p-1, floor((n-1)/2)), continue; otherwise, go to step f) below, c) forming a clean set with any outliers removed and calculating the n clean Studentized residuals t i ext using a regression based on this clean set, where n clean is the size of the clean set, d) labelling points with t i ext > t(α/(2 clean), n clean -p-1) as outliers, and if n o < n o max continue; otherwise, go to step f) below, e) forming a clean set with outliers removed again, if outliers were found in the previous step d). If χ 2 > χ 2 cut, where χ 2 is calculated from regression of the clean set, calculating the n clean Studentized residuals t i ext for all points in the clean set and labelling outliers just as was done in the previous step d), f) if any high-leverage outliers were detected in step b) and n o ≤ n o max, calculating t i pred for these high-leverage points using a regression based on the clean, outlier-free set, and removing the outlier label for this points if t i pred < t(α/2, n clean -p), wherein p is a number of regression parameters and α is a significance level, evaluating the interaction between the analyte in the fluid sample and the ligand using the formed clean set. - providing a first sensor surface, and providing a second sensor surface having a ligand immobilized thereon, - allowing a plurality of fluid samples to flow across said first and said second sensor surface wherein the fluid samples comprise a solvent at known concentration - measuring changes of refractive index at the first and second sensor surface during the flow of each fluid sample - determining a response value and a reference-subtracted response value for each fluid sample based on said changes and creating a data set comprising said values, each fluid sample forming a point in the data set - selecting a reliable set comprising at least five fluid samples from the data set, plotting the response value against the reference-subtracted response value for the reliable set and fitting a curve to them, the curve being used to test the remaining fluid samples from the data set to determine if any of them are outliers and should be removed from the set to increase the quality of the evaluation, said test comprising the steps a)-f) below, said steps being performed in alphabetical order for every other remaining fluid sample in the data set a) calculating leverages h i, b) labelling points with h i > h i cut as outliers, and if the number of outliers, n o, is less than n o max = min(n-p-1, floor((n-1)/2)), continue; otherwise, go to step f) below, c) forming a clean set with any outliers removed and calculating the n clean Studentized residuals t i ext using a regression based on this clean set, where n clean is the size of the clean set, d) labelling points with t i ext > t(α/(2 clean), n clean -p-1) as outliers, and if n o < n o max continue; otherwise, go to step f) below, e) forming a clean set with outliers removed again, if outliers were found in the previous step d). If χ 2 > χ 2 cut, where χ 2 is calculated from regression of the clean set, calculating the n clean Studentized residuals t i ext for all points in the clean set and labelling outliers just as was done in the previous step d), f) if any high-leverage outliers were detected in step b) and n o ≤ n o max, calculating t i pred for these high-leverage points using a regression based on the clean, outlier-free set, and removing the outlier label for this points if t i pred < t(α/2, n clean -p),