 Robin Dawes, returning to Meehl’s question about statistical versus clinical predictions, found that even improper linear models perform better than clinical intuition (Dawes 1979). The distinguishing feature of improper linear models is that the weights of a linear model are selected by some non-optimal method. For instance, equal weights might be assigned to the predictor variables to afford each equal weight or a unit-weight, such as 1 or −1, to tally features supporting a positive or negative prediction, respectively. As an example, Dawes proposed an improper model to predict subjective ratings of marital happiness by couples based on the difference between their rates of lovemaking and fighting. The results? Among the thirty happily married couples, two argued more than they had intercourse. Yet all twelve unhappy couples fought more frequently. And those results replicated in other laboratories studying human sexuality in the 1970s. Both equal-weight regression and unit-weight tallying have since been found to commonly outperform proper linear models on small data sets. Although no simple improper linear model performs well across all common benchmark datasets, for almost every data set in the benchmark there is some simple improper model that performs well in predictive accuracy (Lichtenberg &amp; Simsek 2016). This observation, and many others in the heuristics literature, points to biases of simplified models that can lead to better predictions when used in the right circumstances  (section 4).