# "Observed Power" and other "Power" Issues

**URL:** <https://discourse.datamethods.org/t/observed-power-and-other-power-issues/731>\
**Category:** formal\
**Tags:** power, reporting\
**Created:** [September 26, 2018, 1:11pm UTC](https://discourse.datamethods.org/t/observed-power-and-other-power-issues/731 "2018-09-26T13:11:01Z")\
**Posts on this page:** 1\
**Showing post:** 21

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**Author:** ![ADAlthousePhD](https://discourse.datamethods.org/user_avatar/discourse.datamethods.org/adalthousephd/32/124_2.png) [@ADAlthousePhD](https://discourse.datamethods.org/u/ADAlthousePhD)\
**Post date:** [October 5, 2018, 11:31am UTC](https://discourse.datamethods.org/t/observed-power-and-other-power-issues/731/21 "2018-10-05T11:31:02Z")

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> [@RGNewcombe](#):
>
> However, as others have rightly pointed out, to re-assess power retrospectively by taking the observed effect size as the true value is a total fiction. In fact, posterior power is best regarded as merely a rescaling of the p-value. For example, assume that the study was designed to have an 80% power to detect a specified size of difference using a test at the conventional 2-sided 5% alpha level. Then, if the parameter estimates from the data are just what was assumed in advance, we expect to end up with z or t around 2.8, not 2 - because the planned power was 80%, not 50%. Or, if the eventual test is chi-square, this would be around 8, instead of 4. A retrospective power of 80% simply means that p was about 0.005.

Correct.

It’s amazing how statisticians can spend 20 years writing papers that explain this, and yet the fiction of a retrospective power calculation using the observed effect size lives on in clinical journals as a thing people think is not only useful, but necessary. Even after it’s been explained to them…the response came and showed that they had learned nothing:

[https://insights.ovid.com/pubmed?pmid=29979247](https://insights.ovid.com/pubmed?pmid=29979247)

“We fully understand that P value and post hoc power based on observed effect size are mathematically redundant; **however, we would point out that being redundant is not the same as being incorrect. As such, we believe that post hoc power is not wrong, but instead a necessary first assistant in interpreting results**.”

![coffee%20spit](https://discourse.datamethods.org/uploads/default/original/1X/f9b39f42a645ec127c33e5640458646068ef4921.gif)

@zad and I have just submitted a letter in reply to the surgeon’s double-down. If the journal accepts, I will link; if the journal declines, I will post the full content here, possibly with a guest post on someone’s blog as well.

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