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.
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Level of Confidence (c)
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 95%, which implies that if we were to take many samples and construct confidence intervals, approximately 95% of those intervals would contain the true parameter. This level influences the width of the confidence interval.
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Sample Size (n)
Sample size, denoted as 'n', refers to the number of observations or data points collected in a study. The sample size affects the precision of the estimates and the width of the confidence intervals; larger sample sizes generally lead to more reliable estimates and narrower intervals. In this case, with n = 13, the sample size is relatively small, which may impact the critical values derived from the statistical distribution.
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