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
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.
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Introduction to Confidence Intervals
Sample Size
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.
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