9/ Data cannot help you select between hypotheses, it can only help you eliminate.

The idea that theories can never be proved true, but only be shown to be wrong is core to how science is done.

10/ Scientists definitely know which theories are wrong, but they never know for certain which theories are right.

(Surprisingly, that’s also how VCs work: while investing, they know for sure which companies are “duds” but they never know which ones are going to be “unicorns”).

11/ Box also said: “some (models) are useful”.

Notice that he didn’t say some models are correct.

He used the term “useful”.

12/ The usefulness of models points to their ability to predict the future.

Scientific laws (like Newton’s law of gravitation) are models that help us predict solar eclipses hundreds of years ahead.

13/ Newton's laws work quite well for this purpose while another model like throwing darts to predict eclipses will fail horribly.

So even though both models are wrong, Newton’s law gives us more mileage because it’s proven to be useful in a variety of contexts *that matter*.

14/ TLDR:

- Don’t shoot for being right because there’s no such thing as the “correct” assumptions. Shoot for having “useful” assumptions.

- Data alone is sterile. Whenever you think data is giving you insights, it’s actually the data+your assumptions that are informing you.

15/ That's it! Hope you enjoyed it :)

If you have feedback or comments, do reply.

This thread is a cross-post from my monthly letter on VWO list https://vwo.com/blog/probabilistic-future/