Bausman, William
(2018)
¬¬¬¬Modeling: Neutral, Null, and Baseline.
[Preprint]
Abstract
This paper distinguishes two reasoning strategies for using a model as a “null”. Null modeling evaluates whether a process is causally responsible for a pattern by testing it against a null model. Baseline modeling measures the relative significance of various processes responsible for a pattern by detecting deviations from a baseline model. Scientists sometimes conflate these strategies because their formal similarities, but they must distinguish them lest they privilege null models as accepted until disproved. I illustrate this problem with the neutral theory of ecology and use this as a case study to draw general lessons. First, scientists cannot draw certain kinds of causal conclusions using null modeling. Second, scientists can draw these kinds of causal conclusions using baseline modeling, but this requires more evidence than does null modeling.
Monthly Views for the past 3 years
Monthly Downloads for the past 3 years
Plum Analytics
Actions (login required)
 |
View Item |
![[feed]](http://philsci-archive.pitt.edu/style/images/feed-icon-32x32.png){kind=icon}