In Exercises 3–8, find the critical value(s) and rejection region(s) for the type of t-test with level of significance alpha and sample size n.
Left-tailed test, α=0.10, n=20
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Determine the degrees of freedom (df) for the t-test. The formula for degrees of freedom is df = n - 1, where n is the sample size. In this case, df = 20 - 1.
Identify the level of significance (α) for the test. Here, α = 0.10, which represents the probability of rejecting the null hypothesis when it is true.
Since this is a left-tailed test, the critical value corresponds to the t-score where the cumulative probability to the left of the critical value equals α. Use a t-distribution table or statistical software to find the t-score for df = 19 and α = 0.10.
Define the rejection region. For a left-tailed test, the rejection region is t < critical value, where the critical value is the t-score found in the previous step.
Summarize the critical value and rejection region. Clearly state the critical value and describe the rejection region in terms of the t-statistic (e.g., 'Reject the null hypothesis if t < critical value').
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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Critical Value
A critical value is a point on the scale of the test statistic beyond which we reject the null hypothesis. It is determined based on the significance level (alpha) and the type of test being conducted. For a left-tailed test, the critical value corresponds to the point where the cumulative probability equals alpha, indicating the threshold for rejection.
The rejection region is the range of values for the test statistic that leads to the rejection of the null hypothesis. In a left-tailed test, this region is located to the left of the critical value. If the calculated test statistic falls within this region, it suggests that the sample provides sufficient evidence to reject the null hypothesis at the specified significance level.
A t-test is a statistical test used to determine if there is a significant difference between the means of two groups, especially when the sample size is small and the population standard deviation is unknown. The t-test accounts for sample size and variability, making it suitable for hypothesis testing in various scenarios, including one-sample, independent two-sample, and paired sample tests.