PPT - Sampling Theory - 1

PPT - 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
Learning Objectives :
The different procedures of sampling which will be best
representative of the  population.
The Concept of Sampling Distribution
The techniques of construction of Class Interval & its
interpretation.
How to determine the sample size with pre defined degree of
precision?.
Page 3

Sampling Theory
CPT Section D Quantitative Aptitude
Chapter 15
Prof. Bharat Koshti
Learning Objectives :
The different procedures of sampling which will be best
representative of the  population.
The Concept of Sampling Distribution
The techniques of construction of Class Interval & its
interpretation.
How to determine the sample size with pre defined degree of
precision?.
Why Sampling?

We come across situations whose where we would like to know
But the factors like time, money, cost and large size of the
population make it almost impossible to go for the complete
enumeration of all the population.
Page 4

Sampling Theory
CPT Section D Quantitative Aptitude
Chapter 15
Prof. Bharat Koshti
Learning Objectives :
The different procedures of sampling which will be best
representative of the  population.
The Concept of Sampling Distribution
The techniques of construction of Class Interval & its
interpretation.
How to determine the sample size with pre defined degree of
precision?.
Why Sampling?

We come across situations whose where we would like to know
But the factors like time, money, cost and large size of the
population make it almost impossible to go for the complete
enumeration of all the population.
Sampling Theory
Instead we can select a representative or part of the population
(known as sample) & infer about the entire population on the
basis of our knowledge about the sample.
This is the basis of Sampling Theory.
Page 5

Sampling Theory
CPT Section D Quantitative Aptitude
Chapter 15
Prof. Bharat Koshti
Learning Objectives :
The different procedures of sampling which will be best
representative of the  population.
The Concept of Sampling Distribution
The techniques of construction of Class Interval & its
interpretation.
How to determine the sample size with pre defined degree of
precision?.
Why Sampling?

We come across situations whose where we would like to know
But the factors like time, money, cost and large size of the
population make it almost impossible to go for the complete
enumeration of all the population.
Sampling Theory
Instead we can select a representative or part of the population
(known as sample) & infer about the entire population on the
basis of our knowledge about the sample.
This is the basis of Sampling Theory.
What is sampling ?
It  is a process of learning about a population on the basis of
samples drawn from it.
Some important terms in sampling theory:
• (A) Population
• (B) Sample
• (C) Parameter
• (D) Statistic
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Quantitative Aptitude for CA Foundation

147 videos|175 docs|99 tests

FAQs on PPT - Sampling Theory - 1 - Quantitative Aptitude for CA Foundation

 1. What is sampling theory?
Sampling theory is a branch of statistics that focuses on the selection of a subset (sample) from a larger population to make inferences about the characteristics of the whole population. It provides a framework for understanding how to collect and analyze data efficiently and accurately.
 2. Why is sampling important in research?
Sampling is crucial in research because it allows researchers to study a subset of the population rather than the entire population. This makes data collection more manageable and cost-effective. By using appropriate sampling techniques, researchers can make valid inferences about the population based on the characteristics observed in the sample.
 3. What are the different sampling methods?
There are several sampling methods, including simple random sampling, stratified sampling, cluster sampling, and systematic sampling. Simple random sampling involves randomly selecting individuals from the population without any specific criteria. Stratified sampling involves dividing the population into subgroups (strata) based on certain characteristics and then randomly selecting individuals from each stratum. Cluster sampling involves dividing the population into clusters and randomly selecting entire clusters. Systematic sampling involves selecting individuals from the population at regular intervals.
 4. How do you determine the sample size?
Determining the sample size depends on various factors, such as the desired level of confidence, margin of error, and variability in the population. Generally, a larger sample size provides more accurate results. Researchers often use statistical formulas or software to calculate the sample size needed for a specific study design and research question.
 5. What are the limitations of sampling theory?
Sampling theory assumes that the selected sample is representative of the population, which may not always be the case. Sampling errors, such as non-response bias or selection bias, can occur and affect the validity of the results. Additionally, sampling theory does not account for the complexities of certain populations or situations, such as non-random sampling or small sample sizes. It is crucial for researchers to be aware of these limitations and consider them when interpreting the findings.

Quantitative Aptitude for CA Foundation

147 videos|175 docs|99 tests

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