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Large Sample Chi Square Test for RR in Cohort Studies

This article explores the significance of using the chi-square test in cohort studies to evaluate relative risk (RR). It highlights the challenge posed by one-sided hypotheses, where large values of the chi-square statistic may arise from either large positive or large negative values of the normal statistic. Two methods for calculating the P-value are discussed: utilizing the Z distribution and partitioning the right tail area of the chi-square distribution. Understanding these aspects is crucial for accurate statistical analysis in epidemiological research.

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Large Sample Chi Square Test for RR in Cohort Studies

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  1. Large SampleChi Square Testfor RRin Cohort Studies

  2. =

  3. Should be significant because: Z

  4. But there is a problem here: 1 Sided HA Large values of a chi square statistic that might indicate a significant result can occur from either large positive or large negative values of the normal statistic.

  5. One way to compute P-value: Use Z distribution.

  6. Another way to compute P value: Divide the area in the right tail of the chi square distribution in half.

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