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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 
 
Different Methods of Sampling 
• 1. Probability Sampling Methods 
• 2. Non-Probabilistic Sampling Methods 
• 3. Mixed Sampling Methods 
Page 3


CPT Section D Quantitative Aptitude Chapter 15 
Prof. Bharat Koshti 
 
Different Methods of Sampling 
• 1. Probability Sampling Methods 
• 2. Non-Probabilistic Sampling Methods 
• 3. Mixed Sampling Methods 
(1) Probability Sampling Methods 
In this method there is a fixed/ pre-assigned Probability 
for each member to be selected in the sample from the 
population. 
(a) Simple Random Sampling(SRS): In this method 
there is an equal chance/probability for every member 
being selected in the sample.  
Page 4


CPT Section D Quantitative Aptitude Chapter 15 
Prof. Bharat Koshti 
 
Different Methods of Sampling 
• 1. Probability Sampling Methods 
• 2. Non-Probabilistic Sampling Methods 
• 3. Mixed Sampling Methods 
(1) Probability Sampling Methods 
In this method there is a fixed/ pre-assigned Probability 
for each member to be selected in the sample from the 
population. 
(a) Simple Random Sampling(SRS): In this method 
there is an equal chance/probability for every member 
being selected in the sample.  
There are two methods in Simple Random Sampling- 
One Simple Random Sampling With replacement (SRSWR)& 
another is Simple Random Sampling without 
replacement(SRSWOR). 
Simple Random sampling is useful when- 
(i) The population size is not very large. 
(ii) The population under study is not heterogeneous. 
Page 5


CPT Section D Quantitative Aptitude Chapter 15 
Prof. Bharat Koshti 
 
Different Methods of Sampling 
• 1. Probability Sampling Methods 
• 2. Non-Probabilistic Sampling Methods 
• 3. Mixed Sampling Methods 
(1) Probability Sampling Methods 
In this method there is a fixed/ pre-assigned Probability 
for each member to be selected in the sample from the 
population. 
(a) Simple Random Sampling(SRS): In this method 
there is an equal chance/probability for every member 
being selected in the sample.  
There are two methods in Simple Random Sampling- 
One Simple Random Sampling With replacement (SRSWR)& 
another is Simple Random Sampling without 
replacement(SRSWOR). 
Simple Random sampling is useful when- 
(i) The population size is not very large. 
(ii) The population under study is not heterogeneous. 
5.5 
Person 
Das 
 
0.87487 
Tripathi 0.89068 
Joshi 0.11597 
   Agarwal 0.58635 
Shah  0.34346 
   Purohit 0.24662 
Singhal 
 
0.47609 
     Bhandari 0.08350 
    Kulkarni 0.53542 
Arora  0.37239 
  Gupta 
 
0.73809 
Generate  
Random # 
Person 
1     Bhandari 0.08350 
2 Joshi 0.11597 
3      Purohit  0.24662 
4 Shah  0.34346 
5 
Arora  
0.37239 
Singhal 
 
0.47609 
   Kulkarni 0.53542 
     Agarwal 
0.58635 
Gupta 0.73809 
Das 0.87487 
Tripathi 0.89068 
Sorted  
Random # 
A government income tax auditor wants to 
choose a sample of 5 out of 11 IT returns to 
audit 
 
Read More
147 videos|175 docs|99 tests

FAQs on PPT - Sampling Theory - 2 - 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 items from a larger population. It provides methods and techniques for making inferences about the population based on the information obtained from the sample.
2. Why is sampling important in research?
Ans. Sampling is important in research because it allows researchers to study a subset of the population rather than the entire population. This makes the research more feasible in terms of time, cost, and resources. By using appropriate sampling techniques, researchers can draw conclusions about the population based on the findings from the sample.
3. What are the different types of sampling techniques?
Ans. There are several types of sampling techniques, including simple random sampling, stratified sampling, cluster sampling, systematic sampling, and convenience sampling. Simple random sampling involves randomly selecting individuals from the population. Stratified sampling involves dividing the population into subgroups and then selecting a sample from each subgroup. Cluster sampling involves dividing the population into clusters and randomly selecting some clusters to include in the sample. Systematic sampling involves selecting individuals from the population at regular intervals. Convenience sampling involves selecting individuals who are easily accessible or readily available.
4. How can sampling errors be minimized?
Ans. Sampling errors occur when the sample does not accurately represent the population. To minimize sampling errors, researchers can use techniques such as increasing the sample size, ensuring random selection, using appropriate sampling methods, and minimizing non-response rates. By reducing the potential for bias and ensuring a representative sample, sampling errors can be minimized.
5. What are the advantages of using stratified sampling?
Ans. Stratified sampling has several advantages. It allows researchers to ensure that important subgroups within the population are represented in the sample. This can help improve the accuracy and precision of the estimates made from the sample. Stratified sampling also allows for comparisons to be made between different subgroups within the population. Additionally, it can help reduce sampling errors and increase the efficiency of the sampling process by targeting specific subgroups of interest.
147 videos|175 docs|99 tests
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