In Exercises 27–30, find the critical values and for the level of confidence c and sample size n. c = 0.90, n = 16
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Determine the degrees of freedom (df) for the t-distribution. The formula for degrees of freedom is df = n - 1, where n is the sample size.
Identify the level of confidence (c) and calculate the area in the tails of the t-distribution. For a 90% confidence level, the area in the tails is 1 - c = 0.10, and since the t-distribution is symmetric, divide this area equally between the two tails (0.10 / 2 = 0.05 in each tail).
Use a t-distribution table or a statistical calculator to find the critical t-value corresponding to the degrees of freedom (df) and the area in one tail (0.05).
The critical values for a two-tailed test are the positive and negative of the t-value found in the previous step. These represent the boundaries of the confidence interval.
Verify your results by ensuring that the critical values correspond to the specified confidence level (90%) and sample size (n = 16).
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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Critical Values
Critical values are the points on the scale of the test statistic that define the boundaries for rejecting the null hypothesis. They are determined based on the desired level of confidence and the distribution of the test statistic. For example, in a normal distribution, critical values correspond to specific z-scores that capture the central area of the distribution, reflecting the confidence level.
The level of confidence, denoted as 'c', represents the probability that the confidence interval will contain the true population parameter. A common level of confidence is 90%, which implies that if we were to take many samples and construct confidence intervals, approximately 90% of those intervals would contain the true parameter. This level influences the width of the confidence interval.
Sample size, denoted as 'n', refers to the number of observations or data points collected in a study. It plays a crucial role in statistical analysis, as larger sample sizes generally lead to more reliable estimates of population parameters and narrower confidence intervals. In this case, a sample size of 16 indicates a relatively small sample, which may affect the precision of the confidence interval.