# Some thoughts on uniform prior probabilities when estimating P values and confidence intervals

**URL:** <https://discourse.datamethods.org/t/some-thoughts-on-uniform-prior-probabilities-when-estimating-p-values-and-confidence-intervals/7508>\
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**Created:** [February 8, 2024, 8:52pm UTC](https://discourse.datamethods.org/t/some-thoughts-on-uniform-prior-probabilities-when-estimating-p-values-and-confidence-intervals/7508 "2024-02-08T20:52:54Z")\
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**Author:** ![R\_cubed](https://discourse.datamethods.org/user_avatar/discourse.datamethods.org/r_cubed/32/1518_2.png) [@R\_cubed](https://discourse.datamethods.org/u/R_cubed)\
**Post date:** [June 29, 2025, 11:56pm UTC](https://discourse.datamethods.org/t/some-thoughts-on-uniform-prior-probabilities-when-estimating-p-values-and-confidence-intervals/7508/160 "2025-06-29T23:56:16Z")

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> [@HuwLlewelyn](#):
>
> If I wish to estimate the posterior distribution of beta for the current study, then I do so by assuming a flat prior, b and SE1 = s/root(n1)

**Why** do you assume a flat prior after your first estimate? That amounts to ignoring the likelihood function from the first study.

Please study the following:

**Kass, R. E. (1990).** Data-translated likelihood and Jeffreys’s rules. _Biometrika_, _77_(1), 107-114. ([PDF](https://www.stat.cmu.edu/~kass/papers/jeffreys.pdf))

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