Berkson's paradox is a statistical phenomenon that can occur when using data that has been collected in a biased or unrepresentative way. It refers to the observation that the correlation between two variables can appear to be different when they are studied in different populations or samples. This can lead to incorrect conclusions being drawn about the relationship between the two variables.
For example, consider a study that is conducted to examine the relationship between age and blood pressure. If the study population is limited to only young adults, it might be found that there is no relationship between age and blood pressure. However, if the study is conducted in a more representative sample that includes a wider range of ages, it might be found that there is a positive correlation between age and blood pressure. This is an example of Berkson's paradox, because the apparent relationship between the two variables can change depending on the sample that is used.
It is important to be aware of Berkson's paradox and to consider the representativeness of the sample when drawing conclusions from statistical data. This can help to avoid making incorrect inferences about relationships between variables.
Case Studies:
- [CHG Issue #93: Berkson's Paradox](https://cedarshillgroup.substack.com/p/chg-issue-93-berksons-paradox)
- [CHG Issue #77: Sampling](https://cedarshillgroup.substack.com/p/issue-77-sampling)
Explore Further: [Moontower Meta](https://moontowermeta.com/berksons-paradox/) | [[Simpson's Paradox]]
Tags: #seeds
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