While the EVM model provides unbiased parameter estimates, the errors calculated in are still assumed to be entirely described by the covariance of the parameters in question. While this is asymptotically true, weakly constraining data (e.g. few data points, or data collected in a region of the data space which is not sensitive to the parameters) might render this assumption false. In particular, the non-linearity of the problem always induces non-Gaussianities in the Bayesian posterior. Therefore, the presentation of the errors as symmetric 1σ uncertainties does not show the whole picture. The same is true when plotting confidence intervals as ellipses from the parameter covariance. This is especially important for small reactivity ratios which have an r i value close to 0. Due to the constraints that r i ≥ 0, the probability is shifted to higher values. The described approach does not take this into account an therefore, underestimates the error in higher value areas. Following this situation in current literature, this publication aims to shed light on advantages and disadvantages of the published methods as well as introducing a new approach. An open source code will be provided with this work in order to allow the access to a user-friendly Python Script to enable polymer chemists to review their own as well as published reactivity ratio data with a state of the art approach.