People learn from experience and extrapolate from the relevant past to predict the future. Data-driven regression models do the same thing. To know why, we need to shift our perspective on data. B

I Modern statistics focus on variables: carefully selecting the right ones, measuring their impact and testing their significance. But this approach does not align with the experiential way most peoplethink. We show that it’s possible to reinterpret a linear regression model. The prediction it supplies is equivalent to a weighted average of what happened in the past, where the weight on each observation is its relevance.

The human and statistical versions of relevance consist of two parts: similarity and informativeness. We often rely on observations that are similar to today and different from the norm. This view allows us to overlay judgement and statistics using the language of events, leading to more intuitive and effective predictions.

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