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Note that the design of the experiment needs to be able to disprove theory. That is to say if the results don’t match calculations then their theory is falsified and we make progress.

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Interesting to think that a successful experiment is one that disproves a theory, or, in other words, one where the numbers aren't the ones one expected.

https://en.wikipedia.org/wiki/Falsifiability

A successful experiment can also support a prediction. I.e., "using this model, we predict that a bagel with blueberry cream cheese will remain on the counter for 94+/-4 seconds before disappearing." The team will then release the data from their experiment, which either supports or disproves a model.

This kind of work is critical to refining theories and highlighting/fill gaps in knowledge.

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