First, sorry, I didn’t realize `hypothesis`

actually does almost exactly the same thing I proposed for one-sided hypotheses. The only additional step is that `hypothesis`

converts the probability to evidence ratio (you may note that `0.884 / (1 - 0.884) = 7.62`

).

If you believe your model is correct, it tells you that you should also believe that there is 88% probability that the `eeg1_standard_age`

coefficient is smaller. Nothing more, nothing less.

Overall, I don’t think dichotomous thinking of changes/doesn’t change is very useful. Why wouldn’t the influence change at least a little bit after a correction? For any non-trivial correction I would expect the “influence” to change at least a little 100% of the time. You may be able to evaluate the probability (or evidence ratio, if you prefer) for something like `abs(eeg1_standard_age - eeg1_corrected_age) > some_value_you_consider_important`

, i.e. that the coefficient changes by a noticeable amount.

But I can’t help but wonder, whether the way you ask the question is sensible - what exactly is the correction? If your correction was that `eeg1_corrected = 0.1 * eeg1_standard`

, you would get `eeg1_standard_age * 0.1 = eeg1_corrected_age`

so the coefficients would change (with high probability / evidence ratio), but I don’t think it would be sensible to say that the “influence” of age changes.

It might be more sensible to fit separate models for `standard`

and `corrected`

and compare them with `loo`

or perform posterior predictive checks to see which is better “explained” by age (for some meaning of “explain”).

Does that make sense?