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If the Underlying Populations Cannot Be Assumed to Be Normal

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If the underlying populations cannot be assumed to be normal, then by the central limit theorem, the sampling distribution of If the underlying populations cannot be assumed to be normal, then by the central limit theorem, the sampling distribution of   <sub>1</sub> -   <sub>2</sub> is approximately normal only if the sum of the sample observations is sufficiently large-that is, when n<sub>1</sub> + n<sub>2</sub> ≥ 30. 1 - If the underlying populations cannot be assumed to be normal, then by the central limit theorem, the sampling distribution of   <sub>1</sub> -   <sub>2</sub> is approximately normal only if the sum of the sample observations is sufficiently large-that is, when n<sub>1</sub> + n<sub>2</sub> ≥ 30. 2 is approximately normal only if the sum of the sample observations is sufficiently large-that is, when n1 + n2 ≥ 30.


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