To give an overview regarding the consideration of error structures and equation types, the two published approaches of Van Herk et al. , Scott et al. and the newly presented approach of this work are compared in table . The method presented by Van Herk et al. may be used to infer reactivity ratios for data sources with Gaussian errors in the F 2 -value, while errors in the f 2 are neglected. Hereby, van Herk relies on calculation and investigation of the sum-of-squares. Although the applicable error structure is limited to y-errors (F 2 ± ∆F 2 ), the approach is highly valued among polymer chemists due to its comprehensibility and feasible self-implementation with basic programming skills. The approach presented by Scott et al. is more advanced with consideration in errors in dependent and independent variable. It additionally allows for correlation in errors. In comparison to van Herk, the underlying mathematics are much more intricate and are not comprehensible for standard-chemists who do not possess an in depth knowledge of statistics. Although the approach of Scott et al. is a more advanced approach in determination of confidence contours in reactivity ratios, it does possess certain disadvantages. Hereby, a Taylor expansion is employed in error determination which assumes a Gaussian likelihood. By calculating the second derivative with respect to the parameters of the logarithmic likelihood function, the Gaussian errors for the parameters may be determined. For linear functions, this approach perfectly depicts reality as all higher derivatives are zero. However, this procedure leads to a misrepresentation of errors for non-linear functions since the Taylor expansion does not end with the second derivative. The consideration of higher derivatives is not necessarily helpful for solving the problem as a normalization for the function's integral is no longer possible. Coming from this complication, another approach is employed in this work which is related to the approach of van Herk as it relies on screening contour lines. However, this screening is conducted for the likelihood density function (in opposition of the sum-of-squares approach by . Via the normalized likelihood density function, a contour for a certain probability may be numerically calculated by varying the integration limits. By this, non-Gaussanities may be properly represented by the shape of the confidence intervals. The pathway of the approach presented in this work is schematically shown in figure (see ). This is especially of importance, if the errors are asymmetrically distributed. This may be the case if values (and errors) are correlated for both variables (e.g. if F 2 is calculated from f 2 and not measured separately via spectroscopy). With asymmetrical errors, the standard χ 2 formalism can not be as easily applied for the error propagation. In order to fill this gap and to consider such cases, the presented approach accounts for both errors in f 2 and F 2 , while also allowing for a correlation between the two and being applicable on non-linear equations. The tool presented here is capable to estimate the reactivity ratios and their respective errors to high fidelity for all three error-structures as long as the model can be linearized in the order of magnitude of the error bars. Given this, the likelihood of the data can be determined to be a Gaussian with a modified covariance matrix (Figure , step 1). Using Bayes theorem, the likelihood of the data f 2,i and F 2,i can be connected to the probability of the parameters r 1 and r 2 . For more information on Bayes theorem, marginal and conditional probabilities we refer to appendix A.3. The reactivity ratios are estimated using a maximum likelihood estimator. While the distribution of data was determined by the Gaussian likelihood, the probability distribution of the parameters r 1 and r 2 can be non-Gaussian. Therefore, confidence intervals are not necessarily ellipses and should consequently be referred to as confidence contours in parmeter space.