Every day we encounter explanations of various natural and social phenomena. For instance, we may see a leaf fall from a tree, or witness a dog wince, or hear a loud noise coming from the porch. Each of these call for explanations of various sorts, some of which will be much more probable than others. Which, in your view, is the more plausible explanation for your observation of the leaf falling?
- The leaf fell because of various natural causal factors, such as the presence of a very strong wind combined with the gravitational pull that the earth exerted on the leaf.
- There are ghostly spirits lurking in the ground that desire the nutrients in the leaf and have the power to cause the leaf to fall.
Now, we all know the former is a more plausible explanation, but it is not enough as rational thinkers to merely affirm that this is the case. We need to substantiate why this is the case. This post seeks to provide a sketch of how one can assess explanations.
There are (broadly) five criteria for finding the best explanation.
1) Fruitfulness: has this explanation been put to the (empirical or philosophical) test and yielded results? What is its truth value?
2) Simplicity: the explanation that makes less claims and posits less entities is generally the better one.
Relevant considerations regarding simplicity:
- The number of entities a hypothesis/theory/explanation posits
- The theory’s ontological commitments
- The type of entities a hypothesis posits
- The number of claims made
- The type and complexity of claims made
3) Testability: is the explanation open to confirmation? Is it open to falsification? Has it been confirmed? Is it unfalsifiable? Are the mechanisms specified? Are the mechanisms testable, coherent, and intelligible? Generally speaking, falsifiable hypotheses are more easily testable, and more testable explanations tend to be the better ones.
4) Conservatism: this is how something coheres with what we previously know and how well it is supported by our previously established facts, evidence, and explanations.
5) Explanatory scope: Which hypothesis sufficiently explains the largest amount of data with the most plausible and fitting explanation?
– How well does the hypothesis explain the data?
– How much data does the hypothesis explain?
Most of these are tools, not formal logical laws, however they are very useful guidelines. Such considerations are fantastically helpful when evaluating explanations both in daily life and in philosophical arguments.
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