Identification of Statistical Outliers. Statistical outliers have the potential to significantly skew data analysis and interpretation. To identify such outliers, data was first binned in the concentration space, then the median and quartiles of the distributions were analyzed. Extractant concentration data was aggregated into intervals of 0.05M , with additional ranges for very small concentrations: x ≤ 0.02M and 0.02M > x > 0.05M , while for initial acid concentration, 0.5M intervals were employed. Figure , shows a box plot for each data interval of the aggregation step. The box represents the interquartile range (middle 50% of the data), while the whiskers represent points that extend up to 1.5× the inter-quartile range. Points beyond the whisker are readily identified as outliers. As we can see from the Figure , datapoints that are only reported once are usually identified as outliers (red x symbol) as for values that greatly deviate from the group median (orange lines). These values were excluded from the analysis, where the distributions of the data before and after removal of the outliers is shown in Figure . Outliers removed from the acid-extractant space where not considered in the [acid] ini -[acid]org analysis.