Statistical Estimation and Sampling Variability

The video tutorial explains sampling variability, highlighting that different random samples can yield different estimates. It uses the example of median household income in Virginia to illustrate this concept. The tutorial discusses why a sample median may not equal the population median and explains how increasing the sample size can reduce variability and improve estimate accuracy.

What is the term used to describe the phenomenon where different random samples from the same population yield different estimates?

• Sampling bias

• Sampling variability

• Sampling distribution

• Sampling error

What is the effect of sampling variability on statistical estimates?

• It makes estimates more accurate

• It makes estimates more biased

• It makes estimates less accurate

• It has no effect

What is the term for the difference between sample estimates and true population values?

• Sampling variability

• Sampling bias

• Sampling distribution

• Sampling error

How can sampling variability be reduced?

• By decreasing the sample size

• By increasing the sample size

• By using a biased sample

• By using a non-random sample

What is the main benefit of using a larger sample size in statistical estimation?

• It increases the cost

• It increases the complexity

• It reduces the accuracy

• It reduces the sampling variability

What is the relationship between sample size and sampling variability?

• Exponentially proportional

• No relationship

• Inversely proportional

• Directly proportional

In the example provided, what was the median household income in the sample of Virginia households?

• $60,000

• $61,784

• $62,000

• $63,500

What is the sample size mentioned in the example of Virginia households?

• 250 households

• 1000 households

• 371 households

• 500 households

Why is it unlikely that the sample median is exactly the true median of the population?

• Because the sample size is too large

• Because different samples produce different medians

• Because the sample was not random

• Because the sample was biased

What is the primary reason for the difference in medians from different samples?

• Sampling bias

• Sampling error

• Sampling distribution

• Sampling variability