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 a null hypothesis (H0) and an alternative hypothesis (H1), then using sample data to determine whether to reject H0 in favor of H1. The outcome helps in understanding if there is enough evidence to support a specific claim about the population.
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One-Tailed vs. Two-Tailed Tests
In hypothesis testing, a one-tailed test evaluates the possibility of the relationship in one direction (either greater than or less than), while a two-tailed test assesses both directions. A right-tailed test looks for evidence that a parameter is greater than a certain value, whereas a left-tailed test checks if it is less. The choice between these tests depends on the research question and the nature of the hypothesis.
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Difference in Proportions: Hypothesis Tests
Critical Region
The critical region in hypothesis testing is the set of all values of the test statistic that would lead to the rejection of the null hypothesis. For a right-tailed test, this region is located in the upper tail of the distribution, while for a left-tailed test, it is in the lower tail. In a two-tailed test, the critical regions are found in both tails, reflecting the possibility of extreme values in either direction.
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Critical Values: t-Distribution