Here are the essential concepts you must grasp in order to answer the question correctly.
Hypothesis Testing
Hypothesis testing is a statistical method used to make decisions about a population based on sample data. It involves formulating two competing hypotheses: the null hypothesis (H0), which represents a statement of no effect or no difference, and the alternative hypothesis (Ha), which represents the claim being tested. The goal is to determine whether there is enough evidence to reject H0 in favor of Ha.
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Null and Alternative Hypotheses
The null hypothesis (H0) is a statement that indicates no significant difference or effect exists, serving as a default position. In contrast, the alternative hypothesis (Ha) represents the claim or effect that the researcher aims to support. For the given statement μ < 128, H0 would typically be μ ≥ 128, while Ha would be μ < 128, indicating a claim that the population mean is less than 128.
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Complements of Hypotheses
The complement of a hypothesis refers to the opposite scenario of the original claim. In hypothesis testing, if the alternative hypothesis states a specific condition (e.g., μ < 128), its complement would encompass all other possibilities (e.g., μ ≥ 128). Understanding complements is crucial for correctly formulating H0 and Ha, as they must cover all potential outcomes in the context of the hypothesis being tested.
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