Chapter 7 Sampling Distributions Review

Front

What is a sampling distribution?

Back

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

Front

What is the Central Limit Theorem?

Back

The Central Limit Theorem states that the sampling distribution of the sample mean will be approximately normally distributed if the sample size is sufficiently large, regardless of the population's distribution.

Front

What is the mean of a sampling distribution?

Back

The mean of a sampling distribution is equal to the mean of the population from which the samples are drawn.

Front

What is the standard deviation of a sampling distribution?

Back

Front

How do you determine if a sampling distribution is normal?

Back

A sampling distribution can be considered normal if the sample size is large enough (typically n ≥ 30) or if the population distribution is normal.

Front

What is the relationship between sample size and the shape of the sampling distribution?

Back

As the sample size increases, the shape of the sampling distribution becomes more normal, even if the population distribution is not normal.

Front

What does it mean for a distribution to be right-skewed?

Back

A right-skewed distribution has a longer tail on the right side, meaning that most values are concentrated on the left with fewer high values.

Front

What is the formula for calculating the standard error?

Back

Front

What is the significance of a sample size of 100 in sampling distributions?

Back

A sample size of 100 is often considered large enough for the Central Limit Theorem to apply, leading to an approximately normal sampling distribution.

Front

What does it mean for a distribution to be approximately normal?

Back

A distribution is approximately normal if it resembles a normal distribution, with a bell-shaped curve, even if it is not perfectly normal.