# Statistical Misinterpretations and Clinical Practice Guidelines: any examples?

**URL:** <https://discourse.datamethods.org/t/statistical-misinterpretations-and-clinical-practice-guidelines-any-examples/4372>\
**Category:** health services research\
**Tags:** interpretation\
**Created:** [May 8, 2021, 2:34pm UTC](https://discourse.datamethods.org/t/statistical-misinterpretations-and-clinical-practice-guidelines-any-examples/4372 "2021-05-08T14:34:07Z")\
**Posts on this page:** 1\
**Showing post:** 4

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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:** [May 11, 2021, 2:20pm UTC](https://discourse.datamethods.org/t/statistical-misinterpretations-and-clinical-practice-guidelines-any-examples/4372/4 "2021-05-11T14:20:50Z")

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> Blockquote  
> It seems to me that such cognitive/epistemological errors are far more insidious than technical errors in p-value interpretation, and much more capable of insinuating themselves into CPGs.

Good point. This brings up 2 more questions:

1. What does the clinician do when someone in authority makes policy decisions based upon things they “know” that really aren’t so?

2. In the U.S., Federal regulators have made it clear that they intend to shift financial risk to providers of health services through policies like [value based healthcare](https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/Value-Based-Programs).

How can health service providers make defensible decisions when communities of clinicians and/or regulators are misled by these epistemological errors?

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