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2.2 Sampling & Sampling Distribution — Test 1
Q1. A population, in statistics, refers to the:✓ Entire group of items or individuals under study
Q2. A numerical value that describes a population is called a ____, while one describing a sample is a ____.✓ Parameter; statistic
Q3. The sampling distribution of a statistic is the:✓ Distribution of that statistic over all possible samples
Q4. The standard error of a statistic is the:✓ Standard deviation of its sampling distribution
Q5. The central limit theorem states that, for large samples, the sampling distribution of the mean is approximately:✓ Normal, regardless of the population's shape
Q6. As the sample size increases, the standard error of the mean:✓ Decreases
Q7. In simple random sampling, every member of the population has:✓ An equal chance of being selected
Q8. Stratified sampling involves:✓ Dividing the population into subgroups (strata) and sampling from each
Q9. Systematic sampling selects units by:✓ Choosing every kth item from an ordered list after a random start
Q10. The difference between a sample statistic and the true population parameter is called:✓ Sampling error
Q11. A study that collects data from every member of a population is called a:✓ Census
Q12. Cluster sampling involves:✓ Dividing the population into clusters and randomly selecting whole clusters
Q13. A representative sample is one that:✓ Reflects the characteristics of the whole population
Q14. Bias in sampling refers to:✓ A systematic tendency to over- or under-represent certain outcomes
Q15. The standard error of the mean is calculated as:✓ Population (or sample) SD ÷ √n
Q16. Probability sampling methods differ from non-probability methods in that probability methods:✓ Give each unit a known, non-zero chance of selection
Q17. The main reason sampling is used instead of studying the whole population is that sampling is:✓ More economical and faster, while still allowing valid inference
Q18. The central limit theorem is important because it allows us to:✓ Use normal-based methods for the sample mean even when the population is not normal
Q19. A sample mean is an example of a:✓ Statistic
Q20. Match each sampling concept with its description and select the correct option.✓ A-iii, B-ii, C-i, D-iv