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examples/howto/hypothesis_testing.ipynb

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"cell_type": "code",
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"execution_count": 4,
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"id": "bfaed521",
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"metadata": {},
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"metadata": {
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"tags": [
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examples/howto/hypothesis_testing.myst.md

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Now we'll build our simple model. Again, the focus here is not on the model of the data as such, but simply obtaining a meaningful prior and posterior distribution. We'll ask for more MCMC samples than we normally do, so that we can get a more accurate approximation of the prior and posterior distributions.
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```{code-cell} ipython3
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with pm.Model() as model:
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# priors
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mu = pm.Normal("mu", mu=0, sigma=2)

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