PPT - Sampling Theory - 2

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

``` Page 1

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

```

## Quantitative Aptitude for CA Foundation

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.

## Quantitative Aptitude for CA Foundation

147 videos|175 docs|99 tests

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