All Models are Wrong, but Some are Useful. Especially with the Right Data
All Models are Wrong, but Some are Useful. Especially with the Right Data Free Tutorial Download
All Models Are Wrong
To dig into this statement, we need to define and examine what a model is.
For the context of this article, a model can be thought of as a simplified representation of a system or object. Statistical models approximate patterns in a data set by making assumptions about the data as well as the environment it was gathered in and applied to.
The three broad categories of assumptions made by statistical models are distributional assumptions (assumptions about the distribution of values in a variable or the distribution of observational errors), structural assumptions (assumptions about the functional relationship between variables), and cross-variation assumptions (joint probability distribution).
For example, a linear regression model assumes that the relationships between variables in a data set are linear (and only linear). In the eyes of a linear model, any distance between the observations that make up the data set and the modeled line is just noise (i.e., random or unexplained fluctuations in the data) and can ultimately be ignored.
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