Good Explanations Are Hard to Vary | Summary and Q&A
Summary
In this video, Brett discusses the importance of scientific theories as a subset of good explanations. He emphasizes that testability is key, but it is not enough on its own to determine the quality of an explanation. Being able to vary the explanation without changing the predictions is important, as well as having precise and risky predictions.
Questions & Answers
Q: Is a scientific theory a subset of a good explanation?
Yes, scientific theories are a type of good explanation. They are testable and can be falsified.
Q: Are falsifiable theories common?
Falsifiable theories are actually quite common and easily created. However, the abundance of such theories does not indicate their quality as explanations.
Q: Can you provide an example of a testable theory that is not a good explanation?
One example is the claim that eating 1.0 kilograms of grass can cure the common cold. While this theory is testable, it lacks an explanation of the mechanism behind the cure. Therefore, it does not make any real progress.
Q: Why should a good explanation be hard to vary?
A good explanation should be hard to vary because it needs to be precise. If the explanation can easily be changed or varied without affecting the predictions, it may not provide a solid foundation for understanding the phenomenon.
Q: What is the example of an easy-to-vary explanation?
The Greek explanation of seasons being driven by the goddess Persephone is an example of an easy-to-vary explanation. It could have easily been changed, with different gods and goddesses involved, without affecting the predictions.
Q: What is the significance of the axis tilt theory for explaining seasons on Earth?
The axis tilt theory states that the Earth is angled at 23 degrees relative to the sun, which causes different seasons. This theory makes risky and narrow predictions, allowing for precise testing. It accurately predicts the length of summer and winter at different latitudes.
Q: Why should a good explanation have precise predictions?
Precise predictions are essential because they allow for accurate testing and verification. If the predictions are vague or imprecise, it becomes challenging to confirm or refute the explanation.
Q: Can you explain what it means for predictions to be risky?
Risky predictions are those that are not obvious or easily expected. They go against common knowledge or intuition and require specific conditions or circumstances to be true. These predictions are valuable because they can provide evidence for or against an explanation.
Q: Can you provide an example of a risky prediction?
An example of a risky prediction is the bending of starlight around an eclipse, as predicted by Einstein's theory of general relativity. This prediction was not intuitive and took a long time to confirm, but it ultimately provided support for the theory.
Q: Why is it important not to vary a theory after the fact, like in the grass example?
Varying a theory after the fact undermines its credibility and scientific integrity. Changing the parameters or conditions of a theory to fit the observed results is not scientifically sound and can lead to flawed conclusions.
Takeaways
In summary, a good scientific explanation should be testable, falsifiable, hard to vary, and have precise and risky predictions. Testability alone is not enough, as the quality of an explanation depends on the precision and riskiness of its predictions. Additionally, varying a theory after the fact can lead to unreliable results.
