MCQ - Sampling Theory - 1

# MCQ - Sampling Theory - 1 | Quantitative Aptitude for CA Foundation PDF Download

``` Page 1

Sampling Theory
CPT Section D Quantitative Aptitude
Chapter 15
Prof. Bharat Koshti
Page 2

Sampling Theory
CPT Section D Quantitative Aptitude
Chapter 15
Prof. Bharat Koshti
Question Time
MCQ's
Page 3

Sampling Theory
CPT Section D Quantitative Aptitude
Chapter 15
Prof. Bharat Koshti
Question Time
MCQ's
1. Statistical data may be collected by
complete  enumeration is called

(a) Census inquiry
(b) Sample inquiry
(c) both
(d) none
Page 4

Sampling Theory
CPT Section D Quantitative Aptitude
Chapter 15
Prof. Bharat Koshti
Question Time
MCQ's
1. Statistical data may be collected by
complete  enumeration is called

(a) Census inquiry
(b) Sample inquiry
(c) both
(d) none

2. Sampling can be described as a
statistical procedure

(a) To infer about the unknown universe from a knowledge of any
sample.
(b) To infer about the known universe from a knowledge of a
sample drawn from it.
(c) To infer about the unknown universe from a knowledge of a
random sample drawn from it.
(d) Both (a) and (b).
Page 5

Sampling Theory
CPT Section D Quantitative Aptitude
Chapter 15
Prof. Bharat Koshti
Question Time
MCQ's
1. Statistical data may be collected by
complete  enumeration is called

(a) Census inquiry
(b) Sample inquiry
(c) both
(d) none

2. Sampling can be described as a
statistical procedure

(a) To infer about the unknown universe from a knowledge of any
sample.
(b) To infer about the known universe from a knowledge of a
sample drawn from it.
(c) To infer about the unknown universe from a knowledge of a
random sample drawn from it.
(d) Both (a) and (b).

3. Statistical decision about an unknown
universe is  taken on the basis of

(a) Sample observations
(b) A sampling frame
(c) Sample survey
(d) Complete enumeration
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## Quantitative Aptitude for CA Foundation

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## FAQs on MCQ - Sampling Theory - 1 - Quantitative Aptitude for CA Foundation

 1. What is sampling theory?
Ans. Sampling theory is a branch of statistics that deals with the selection of a subset of individuals or objects from a larger population, in order to make inferences about the entire population. It provides methods and techniques to ensure that the selected sample is representative of the population, allowing for generalizations and predictions to be made.
 2. Why is sampling important in research?
Ans. Sampling is important in research because it is often impractical or impossible to collect data from an entire population. By selecting a smaller sample, researchers can still obtain reliable and valid results that can be generalized to the larger population. Sampling also helps in reducing costs, time, and resources required for data collection.
 3. What are the different types of sampling methods?
Ans. There are several types of sampling methods, including simple random sampling, stratified sampling, cluster sampling, systematic sampling, and convenience sampling. - Simple random sampling involves randomly selecting individuals from the population, ensuring that each individual has an equal chance of being included in the sample. - Stratified sampling involves dividing the population into subgroups or strata based on certain characteristics and then selecting proportional samples from each stratum. - Cluster sampling involves dividing the population into clusters or groups, and then randomly selecting entire clusters for inclusion in the sample. - Systematic sampling involves selecting every nth individual from the population after a random starting point is determined. - Convenience sampling involves selecting individuals who are readily available and convenient to include in the sample, which may introduce bias.
 4. How does sample size affect the accuracy of a study?
Ans. Sample size plays a crucial role in the accuracy and precision of a study. Generally, a larger sample size tends to provide more accurate and reliable results, as it reduces the impact of random variation and increases the representativeness of the sample. A smaller sample size may lead to wider confidence intervals and less precise estimates. However, the required sample size depends on various factors such as the research objectives, population size, desired confidence level, and expected effect size.
 5. What is sampling error?
Ans. Sampling error refers to the difference between the characteristics or attributes observed in a sample and the true characteristics or attributes that exist in the population. It is inevitable due to the random variation in the selection process and can occur even if a sample is selected using appropriate sampling techniques. By quantifying the sampling error, researchers can assess the reliability and validity of their findings and make appropriate inferences about the population.

## Quantitative Aptitude for CA Foundation

147 videos|175 docs|99 tests

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