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Data graphs and mechanistic explanation.
1Tulane University, Philosophy Department, 105 Newcomb Hall, New Orleans, LA 70118, USA.
Studies in History and Philosophy of Biological and Biomedical Sciences
|February 13, 2016
Summary
Scientific data representations, like graphs, are often explanatory in themselves, not just evidence for mechanistic explanations. This challenges the traditional view in the philosophy of science.
Area of Science:
- Philosophy of Science
- Biology
- Chronobiology
Background:
- A common assumption in the philosophy of science is that data representations are not explanatory.
- These representations are often viewed as preliminary steps towards a full explanation, typically involving a mechanism.
Purpose of the Study:
- To challenge the assumption that data representations are not explanatory.
- To provide a more accurate account of explanatory practices in biological research, particularly in mechanistic investigations.
- To explore the role of data representations in scientific explanation.
Main Methods:
- Analysis of representational and explanatory practices in mammalian chronobiology.
- Examination of how data graphs are used to represent relationships between quantities across different conditions.
Main Results:
- Biologists frequently use data representations in explanatory ways that go beyond simply providing evidence for mechanistic models.
- Data graphs can be essential for explaining specific phenomena by illustrating relationships between variables.
- The study found that data representations are not merely stepping stones but can be integral to explanation.
Conclusions:
- The assumption that data representations lack explanatory power is unsustainable.
- Scientific explanation in biology involves constructing diverse representations for specific explanatory goals.
- There is no single, explanatorily "fundamental" type of representation in biology; rather, explanation relies on the varied use of different representational forms.
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