# Canonical references for different types of "bias"

**URL:** <https://discourse.datamethods.org/t/canonical-references-for-different-types-of-bias/5571>\
**Category:** descriptive\
**Tags:** design, interpretation\
**Created:** [May 5, 2022, 1:30pm UTC](https://discourse.datamethods.org/t/canonical-references-for-different-types-of-bias/5571 "2022-05-05T13:30:26Z")\
**Posts on this page:** 1\
**Showing post:** 5

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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 5, 2022, 3:37pm UTC](https://discourse.datamethods.org/t/canonical-references-for-different-types-of-bias/5571/5 "2022-05-05T15:37:18Z")

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I remember reading something about this by @sander awhile back, and posted some links in this thread; the articles make a subtle distinction between bias and confounding..

> [@"Exchangeability" in observational studies versus RCTs](https://discourse.datamethods.org/t/exchangeability-in-observational-studies-versus-rcts/3371):
>
> Introductory epidemiology courses that physicians take as part of their EBM training don’t always explicitly discuss exchangeability. Yet it seems like this concept might be important for us to understand (in at least a crude way) when reading medical studies. Could misunderstanding and/or poor/inconsistent teaching around this topic underlie some of our bad critical appraisal habits (e.g., comparing columns in Table 1 of RCTs)? Written explanations of exchangeability become complicated very qu…

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