Abstract
There are two cultures in the use of statistical modeling to reach\nconclusions from data. One assumes that the data are generated by a given\nstochastic data model. The other uses algorithmic models and treats the data\nmechanism as unknown. The statistical community has been committed to the\nalmost exclusive use of data models. This commitment has led to irrelevant\ntheory, questionable conclusions, and has kept statisticians from working on a\nlarge range of interesting current problems. Algorithmic modeling, both in\ntheory and practice, has developed rapidly in fields outside statistics. It can\nbe used both on large complex data sets and as a more accurate and informative\nalternative to data modeling on smaller data sets. If our goal as a field is to\nuse data to solve problems, then we need to move away from exclusive dependence\non data models and adopt a more diverse set of tools.
Keywords
Computer scienceData setField (mathematics)Statistical modelSet (abstract data type)Range (aeronautics)Data scienceStatistical theoryEconometricsData miningMachine learningArtificial intelligenceStatisticsMathematics
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Publication Info
- Year
- 2001
- Type
- article
- Volume
- 16
- Issue
- 3
- Citations
- 4037
- Access
- Closed
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Cite This
Leo Breiman
(2001).
Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author).
Statistical Science
, 16
(3)
.
https://doi.org/10.1214/ss/1009213726
Identifiers
- DOI
- 10.1214/ss/1009213726