AP Statistics: Understanding Bias and Variability

What does an unbiased estimator indicate?

• Estimates that consistently overestimate the population parameter

• Estimates that are always equal to the sample mean

• Estimates that are on average as close as possible to the true population parameter

• Estimates that have no variability

If a sample mean height is equal to the population mean height, the estimator is considered:

• Biased

• Unbiased

• Skewed

• Inconsistent

What condition indicates a sample has minimum variability?

• All samples have statistics that are approximately equivalent to one another

• The sample size is small

• The sample is biased

• All samples have statistics significantly different from one another

Tags

CCSS.7.SP.C.7B

What is skewness a measure of?

• The bias in a sample

• The symmetry of a distribution

• The accuracy of an estimator

• The size of a sample

A distribution is considered skewed if:

• It is a bell curve

• It is symmetric

• It has low variability

• More values are clustered on one side

What does high variability in a sampling distribution indicate?

• Low bias

• More spread out distribution

• High accuracy

• No sampling error

Tags

CCSS.7.SP.C.7B

What is a consistent estimator?

• An estimator that becomes more accurate as the sample size decreases

• An estimator with no bias

• An estimator that becomes more accurate as the sample size increases

• An estimator that produces the same value regardless of sample size

What does nonresponse bias refer to?

• Bias caused by a large sample size

• Bias in choosing a sample

• Distortion of results due to lack of variability

• Distortion of results caused by individuals not responding

Tags

CCSS.HSS.IC.B.3

A right-skewed distribution is characterized by:

• A longer tail on the left side

• A longer tail on the right side

• More extreme values on the left side

• Uniform distribution of values

What is the role of the standard deviation in a sampling distribution?

• To measure the variability of the distribution

• To reduce the sampling error

• To measure the bias of the distribution

• To estimate the population mean