MCQ - Sampling Theory - 2

# MCQ - Sampling Theory - 2 | Quantitative Aptitude for CA Foundation PDF Download

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CPT Section D Quantitative Aptitude Chapter 15
Prof. Bharat Koshti

Page 2

CPT Section D Quantitative Aptitude Chapter 15
Prof. Bharat Koshti

(a) Block or cluster sampling
(b) Area sampling
(c) Quota sampling
(d) Deliberate, purposive or judgment sampling.
Page 3

CPT Section D Quantitative Aptitude Chapter 15
Prof. Bharat Koshti

(a) Block or cluster sampling
(b) Area sampling
(c) Quota sampling
(d) Deliberate, purposive or judgment sampling.
(a) A probabilistic sampling
(b) A non- probabilistic sampling
(c) A mixed sampling
(d) Both (b) and (c).
Page 4

CPT Section D Quantitative Aptitude Chapter 15
Prof. Bharat Koshti

(a) Block or cluster sampling
(b) Area sampling
(c) Quota sampling
(d) Deliberate, purposive or judgment sampling.
(a) A probabilistic sampling
(b) A non- probabilistic sampling
(c) A mixed sampling
(d) Both (b) and (c).
(a) Sample size is proportional to the  population size
(b) Sample size is proportional to the sample SD
(c) Sample size is proportional to the sample variance
(d) Population size is proportional to the  sample variance.
Page 5

CPT Section D Quantitative Aptitude Chapter 15
Prof. Bharat Koshti

(a) Block or cluster sampling
(b) Area sampling
(c) Quota sampling
(d) Deliberate, purposive or judgment sampling.
(a) A probabilistic sampling
(b) A non- probabilistic sampling
(c) A mixed sampling
(d) Both (b) and (c).
(a) Sample size is proportional to the  population size
(b) Sample size is proportional to the sample SD
(c) Sample size is proportional to the sample variance
(d) Population size is proportional to the  sample variance.
(a) Simple random sampling
(b) Multistage sampling
(c) Stratified sampling
(d) Systematic sampling
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## Quantitative Aptitude for CA Foundation

147 videos|175 docs|99 tests

## FAQs on MCQ - Sampling Theory - 2 - Quantitative Aptitude for CA Foundation

 1. What is sampling theory?
Sampling theory is a branch of statistics that deals with the selection of a subset of individuals from a population for the purpose of making inferences about the whole population. It provides a framework for understanding how to obtain representative samples and make accurate predictions or estimates based on those samples.
 2. Why is sampling important in research?
Sampling is important in research because it allows researchers to study a smaller group of individuals that represent the larger population. By selecting a sample carefully, researchers can make valid inferences and generalizations about the entire population, saving time and resources compared to studying the entire population.
 3. What are the advantages of random sampling?
Random sampling is a technique where each member of the population has an equal chance of being selected for the sample. Some advantages of random sampling include: - Reduced bias: Random sampling helps to minimize bias and ensure that the sample is representative of the population. - Generalizability: Random sampling allows for making inferences about the entire population, as each member has an equal chance of being included. - Statistical validity: Random sampling provides a solid foundation for applying statistical tests and drawing accurate conclusions from the sample data.
 4. What is the difference between probability and non-probability sampling?
Probability sampling involves selecting a sample from a population using a random process, ensuring that each member of the population has a known probability of being included. Non-probability sampling, on the other hand, does not involve random selection and relies on the researcher's judgment or convenience. Probability sampling allows for making accurate statistical inferences about the population, while non-probability sampling is more suitable for exploratory research or when it is difficult to obtain a representative sample.
 5. What is the sampling error?
Sampling error is the difference between the characteristics of a sample and the characteristics of the population from which it is drawn. It is an unavoidable error that occurs due to the natural variation between samples. The sampling error can be reduced by increasing the sample size, as larger samples tend to provide more accurate estimates of the population parameters.

## Quantitative Aptitude for CA Foundation

147 videos|175 docs|99 tests

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