If science has not become a cult why do people get triggered if you don't believe the standard model?

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[–]Dragonerne 5 insightful - 1 fun5 insightful - 0 fun6 insightful - 1 fun -  (5 children)

The problem with the Standard Model is that it is amazingly accurate for what it explains

This is called overfitting. Accuracy is not a goal, predictability is the goal.

[–]Alphix 2 insightful - 1 fun2 insightful - 0 fun3 insightful - 1 fun -  (0 children)

And it fails horribly at that.

[–]weavilsatemyface 1 insightful - 1 fun1 insightful - 0 fun2 insightful - 1 fun -  (3 children)

This is called overfitting. Accuracy is not a goal, predictability is the goal.

Of course accuracy is a very important goal. When comparing two theories, if all else is equal, we prefer the more accurate theory over the less accurate one. We certainly don't want inaccurate theories no matter how elegant they are, or how many wrong predictions they make.

The Standard Model is very good at making accurate predictions. What it is not good at is making predictions outside of the SM paradigm. You can get an idea of where the SM falls down here.

I realise that that there is no particular reason why the universe should be simple enough for us to understand, but I can't help but feel that the SM and all its associated theories are epicycles upon epicycles. Nor am I convinced that either Dark Matter or Dark Energy are real.

[–]Dragonerne 1 insightful - 1 fun1 insightful - 0 fun2 insightful - 1 fun -  (2 children)

What it is not good at is making predictions outside of the SM paradigm

It generalizes poorly. A typical sign of overfitting to the known data set.

And no, you are very wrong. When creating models, accuracy of the training data set is NOT the goal. The goal is predictability of data sets outside of the training set.

[–]weavilsatemyface 1 insightful - 1 fun1 insightful - 0 fun2 insightful - 1 fun -  (1 child)

When creating models, accuracy of the training data set is NOT the goal.

You're writing about machine learning, not science. There is no "training set" in the Standard Model, it was designed by humans (not a neural network) decades ago.

[–]Dragonerne 1 insightful - 1 fun1 insightful - 0 fun2 insightful - 1 fun -  (0 children)

A model created by a human or a learning algorithm has to adhere to the same principles. In the end, you end up with a model, how you ended up with this model is irrelevant. The importance is whether this model describes the data correctly. To describe the data correctly, you have to generalize well from the training data to the test data.
You can make a model that gives 100% accuracy but it wont represent reality