In this article Leo Breiman describes two approaches in statistics: One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown.

Statisticians in applied research consider data modeling as the template for statistical analysis and focus within their range of multivariate analysis tools on discriminant analysis and logistic regression in classification and multiple linear regression in regression. This approach has the plus that it produces a simple and understandable picture of the relationship between the input variables and response. But the assumption that the data model is an emulation of nature is not necessarily right and can lead to wrong conclusions.

The algorithmic approach uses neural nets and decision trees; predictive accuracy as criterion to judge the quality of the results of analysis. This approach does not apply data models to explain the relationship between input variable x and output variable y, but treats this relationship as a black box. Hence the focus is on finding an algorithm f(x) such that for future x in a test set, f(x) will be a good predictor of y. While this approach has seen major advances in machine learning, it lacks interpretability of the relationship between prediction and response variables.

This article has been published in 2001, when the word “Big Data” was not yet in everybody’s mouth. But by shaping two different cultures to analyzing data and balancing pros and cons of each approach, it makes the differences of big data analysis in contrast to stochastic data models understandable even to laymen.

Leo Breiman, Statistical Modeling: The Two Cultures. In: Statistical Science, Vol. 16 (2001), No. 3, 199-231. Freely vailable online here.