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Exact unconditional inference for analyzing contingency tables in finite populations
Shiva S Dibaj1, Alan D Hutson2, Graham W Warren3
1Janssen Pharmaceutical Companies of Johnson and Johnson, San Diego, CA, USA.
Journal of Applied Statistics
|June 16, 2022
Summary
Researchers developed an exact unconditional test for comparing two proportions in finite populations. This new statistical method maintains significance levels while offering equal power to existing tests for complex population structures.
Area of Science:
- Statistics
- Population Studies
Background:
- Exact inferential methods are increasingly popular due to advances in computing power.
- Methodologies for complex structures like finite populations are lacking.
- Hypergeometric distribution models samples from finite populations.
Purpose of the Study:
- To develop an exact unconditional test for comparing two proportions in finite populations.
- To address the methodological gap for complex population structures.
Main Methods:
- Developed an exact unconditional test.
- Utilized sample information to restrict the search for maximum p-value.
Main Results:
- The proposed test maintains pre-specified nominal significance levels.
- The test achieves power equal to its competitors.
Conclusions:
- The new exact unconditional test is suitable for comparing proportions in finite populations.
- This method provides a statistically sound approach for complex population structures.
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