Here are the essential concepts you must grasp in order to answer the question correctly.
Central Limit Theorem
The Central Limit Theorem states that the distribution of sample means will approach a normal distribution as the sample size increases, regardless of the population's distribution, provided the sample size is sufficiently large (typically n ≥ 30). This theorem is crucial for calculating probabilities related to sample means.
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Standard Error
Standard Error (SE) measures the dispersion of sample means around the population mean. It is calculated as the population standard deviation divided by the square root of the sample size (SE = σ/√n). Understanding SE is essential for determining how much variability to expect in sample means and for calculating probabilities.
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Z-scores
A Z-score indicates how many standard deviations an element is from the mean. It is calculated using the formula Z = (X - μ) / SE, where X is the sample mean, μ is the population mean, and SE is the standard error. Z-scores are used to find probabilities in a standard normal distribution, helping to assess whether a sample mean is unusual.
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Z-Scores From Given Probability - TI-84 (CE) Calculator