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 higher confidence level, such as 0.98, indicates a greater certainty that the interval includes the parameter, but it also results in a wider interval. This concept is crucial for understanding how confident we can be in our estimates based on sample data.
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Sample Size
Sample size, denoted as 'n', refers to the number of observations or data points collected in a study. It plays a significant role in statistical analysis, as larger sample sizes generally lead to more reliable estimates and narrower confidence intervals. In this context, a sample size of 25 indicates the number of individuals or items from which data is gathered to estimate the population parameters.
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