ch 7 Intro to Sampling Distribution Review

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What is a sampling distribution?

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A sampling distribution is the probability distribution of a statistic (like the sample mean or sample proportion) obtained from a large number of samples drawn from a specific population.

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What is the Central Limit Theorem?

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The Central Limit Theorem states that the sampling distribution of the sample mean will be approximately normally distributed, regardless of the population's distribution, as long as the sample size is sufficiently large (usually n ≥ 30).

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What is the margin of error in a survey?

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The margin of error is a statistic that expresses the amount of random sampling error in a survey's results. It indicates the range within which the true population parameter is expected to fall.

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How do you calculate a confidence interval?

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A confidence interval is calculated using the formula: CI = sample statistic ± (critical value * standard error).

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What does a 95% confidence interval mean?

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A 95% confidence interval means that if we were to take many samples and build a confidence interval from each sample, approximately 95% of those intervals would contain the true population parameter.

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What is the standard deviation of a sampling distribution?

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The standard deviation of a sampling distribution, also known as the standard error, measures the dispersion of sample means around the population mean.

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What is the difference between a population and a sample?

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A population includes all members of a specified group, while a sample is a subset of the population selected for analysis.

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What is a parameter in statistics?

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A parameter is a numerical characteristic or measure of a population, such as a population mean or population proportion.

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What is a statistic in statistics?

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A statistic is a numerical characteristic or measure obtained by using the data values from a sample.

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What is the shape of the sampling distribution of the sample mean for large samples?

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For large samples, the sampling distribution of the sample mean is approximately normal due to the Central Limit Theorem.