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Page 1 CPT Section D Quantitative Aptitude Chapter 15 Prof. Bharat Koshti Page 2 CPT Section D Quantitative Aptitude Chapter 15 Prof. Bharat Koshti MCQ’s Page 3 CPT Section D Quantitative Aptitude Chapter 15 Prof. Bharat Koshti MCQ’s (a) Only one (b) Two (c) Three (d) Many Answer: D Page 4 CPT Section D Quantitative Aptitude Chapter 15 Prof. Bharat Koshti MCQ’s (a) Only one (b) Two (c) Three (d) Many Answer: D MCQ.2: The most commonly used confidence interval is (a) 95 percent (b) 90 percent (c) 94 percent (d) 98 percent Answer: A Page 5 CPT Section D Quantitative Aptitude Chapter 15 Prof. Bharat Koshti MCQ’s (a) Only one (b) Two (c) Three (d) Many Answer: D MCQ.2: The most commonly used confidence interval is (a) 95 percent (b) 90 percent (c) 94 percent (d) 98 percent Answer: A MCQ.3:It is known that the population standard deviation in waiting of getting PAN card is 13 days. How large a sample should be taken to be 99% confident that the waiting time is within 8 days of true average? (use z = 2.58) (a) 18 days (b) 13 days (c) 19 days (d) 14 days Answer: ARead More
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FAQs on MCQ - Sampling Theory - 3 - SSC CGL Tier 2 - Study Material, Online Tests, Previous Year
| 1. What is sampling theory? |  |
Ans. Sampling theory is a branch of statistics that focuses on the selection and analysis of a subset of individuals or items from a larger population. It provides a framework for making inferences about the population based on the information obtained from the sample.
| 2. Why is sampling theory important in statistical analysis? | |
Ans. Sampling theory is important in statistical analysis because it allows researchers to draw conclusions about a population without having to study every individual or item within that population. By carefully selecting a representative sample, researchers can make accurate inferences and generalizations about the entire population.
| 3. What are the different methods of sampling in sampling theory? | |
Ans. There are several methods of sampling in sampling theory, including simple random sampling, stratified sampling, cluster sampling, and systematic sampling. Simple random sampling involves randomly selecting individuals from the population, while stratified sampling involves dividing the population into homogeneous subgroups and then randomly selecting individuals from each subgroup. Cluster sampling involves randomly selecting groups or clusters from the population, and systematic sampling involves selecting individuals at regular intervals from a list or sequence.
| 4. What is the difference between a sample and a population in sampling theory? | |
Ans. In sampling theory, a population refers to the entire group of individuals or items that researchers are interested in studying. A sample, on the other hand, is a subset of the population that is selected for analysis. The goal of sampling is to obtain a representative sample that accurately reflects the characteristics of the population.
| 5. How does sample size affect the accuracy of results in sampling theory? | |
Ans. Sample size plays a crucial role in the accuracy of results in sampling theory. Generally, larger sample sizes tend to provide more accurate estimates of population parameters. As the sample size increases, the sampling error decreases, leading to more precise and reliable results. However, there is a trade-off between sample size and cost, as larger samples may be more time-consuming and expensive to collect and analyze. Researchers must carefully consider the balance between cost and accuracy when determining the appropriate sample size.
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