STATISTICS FOR ECONOMISTS
A TEACHING MATERIAL FOR
STUDENTS MAJORING IN ECONOMICS
Module II
Department of Economics
Faculty of Business and Economics
Mekelle University
2020
Mekelle
Page 1 of 57
,Chapter V
Sampling and Sampling Distribution.
In statistics, Sampling plays vital role. The overall purpose of this chapter is to introduction and
equip students with the concept of sampling and sampling distribution.
Specifically, the objectives in this chapter are:
To Discuss what sampling is? Why sampling? And explain the different types of
sampling technique/ or design.
To describe the concept of sampling distribution and elaborate the different types
of sampling distribution (i.e., sampling distribution of the sample mean and
sample proportion).
1.4 Introduction to Sampling
At the out set of this course, we have defined what sample and population mean.
Restating again, instant population refers to all items that have been chosen for study.
While sample refers to a portion or subset of the population selected.
Example: In s study of academic performance of first year economics students. The
GPA of all first year economics students is the population.
The GPA Gs some of the first year economics student, is the sample.
Sometimes it is possible and practical to examine every person or item in the population
we wish to study. We call this complete enumeration or census.
Sampling: - is selecting sampler (or part of the items from the population) from
populations.
Page 2 of 57
, Why sampling? (see on the back of this page) Mathematically, we can describe
samples and populations by using measures such as the mean, median, mode, and
standard deviation. When these terms describe the characteristics of a sample,
they are called statistics. When they describe the characteristics of a population,
they are referred parameters.
Example: If the mean CGPA of all first year economics students is 3.32. In this case,
3.32 is the characteristics of the population “All 1st year economics students” and is
termed as population parameter. On the other hand, if we say that the mean CGPA of the
first year economics students at Adi-Haki campus is 3.32, we are using 3.32 to describe a
characteristic of the sample “First year economics students at Adi-Haki campus”. In this
case, 3.32 would be a sample statistics.
If we are convinced that the sample statistics are accurate estimate of the population
characteristics, we could use sample statistics to estimate the population parameter without
measuring the entirety of the items under study.
In order to be consistent, tacticians use lower case roman letters to denote sample statistics, and
Greak or capital letters to denote population parameters. Table 4.1 elow reveals summaries of
the definitions and the symbols.
Page 3 of 57
, Table 4.1 Summary of the difference between populations and Samples
Population Sample
Definition Collection of all subject of
items being dealt the population
Characteristics “Parameter” “Statistics”
Symbols Population Size = N Sample size = n
Population Size = Sample size = x
Population standard Sample standard
Population Deviations = S
Type of Sampling
In statistics, there are two methods of selecting samples from populations: Random or
probability sampling, and Non-random, non-probability or judgment sampling.
(I) probability (Random) Sampling:- is sampling when all items (i.e., each element) in
the population have a chance of being chosen in the sample and the probability of
each element of the population included in the sample is known. There are several
probability sampling technique that will be discussed later.
(II) Non-probability (Non-random/Judgment) sampling is a sampling methodology
where personal knowledge and opinion play major role in identifying which
elements of the population are to be included in the sample, and the probability of
an element from the population to be included in the sample is not known. Just
Page 4 of 57
A TEACHING MATERIAL FOR
STUDENTS MAJORING IN ECONOMICS
Module II
Department of Economics
Faculty of Business and Economics
Mekelle University
2020
Mekelle
Page 1 of 57
,Chapter V
Sampling and Sampling Distribution.
In statistics, Sampling plays vital role. The overall purpose of this chapter is to introduction and
equip students with the concept of sampling and sampling distribution.
Specifically, the objectives in this chapter are:
To Discuss what sampling is? Why sampling? And explain the different types of
sampling technique/ or design.
To describe the concept of sampling distribution and elaborate the different types
of sampling distribution (i.e., sampling distribution of the sample mean and
sample proportion).
1.4 Introduction to Sampling
At the out set of this course, we have defined what sample and population mean.
Restating again, instant population refers to all items that have been chosen for study.
While sample refers to a portion or subset of the population selected.
Example: In s study of academic performance of first year economics students. The
GPA of all first year economics students is the population.
The GPA Gs some of the first year economics student, is the sample.
Sometimes it is possible and practical to examine every person or item in the population
we wish to study. We call this complete enumeration or census.
Sampling: - is selecting sampler (or part of the items from the population) from
populations.
Page 2 of 57
, Why sampling? (see on the back of this page) Mathematically, we can describe
samples and populations by using measures such as the mean, median, mode, and
standard deviation. When these terms describe the characteristics of a sample,
they are called statistics. When they describe the characteristics of a population,
they are referred parameters.
Example: If the mean CGPA of all first year economics students is 3.32. In this case,
3.32 is the characteristics of the population “All 1st year economics students” and is
termed as population parameter. On the other hand, if we say that the mean CGPA of the
first year economics students at Adi-Haki campus is 3.32, we are using 3.32 to describe a
characteristic of the sample “First year economics students at Adi-Haki campus”. In this
case, 3.32 would be a sample statistics.
If we are convinced that the sample statistics are accurate estimate of the population
characteristics, we could use sample statistics to estimate the population parameter without
measuring the entirety of the items under study.
In order to be consistent, tacticians use lower case roman letters to denote sample statistics, and
Greak or capital letters to denote population parameters. Table 4.1 elow reveals summaries of
the definitions and the symbols.
Page 3 of 57
, Table 4.1 Summary of the difference between populations and Samples
Population Sample
Definition Collection of all subject of
items being dealt the population
Characteristics “Parameter” “Statistics”
Symbols Population Size = N Sample size = n
Population Size = Sample size = x
Population standard Sample standard
Population Deviations = S
Type of Sampling
In statistics, there are two methods of selecting samples from populations: Random or
probability sampling, and Non-random, non-probability or judgment sampling.
(I) probability (Random) Sampling:- is sampling when all items (i.e., each element) in
the population have a chance of being chosen in the sample and the probability of
each element of the population included in the sample is known. There are several
probability sampling technique that will be discussed later.
(II) Non-probability (Non-random/Judgment) sampling is a sampling methodology
where personal knowledge and opinion play major role in identifying which
elements of the population are to be included in the sample, and the probability of
an element from the population to be included in the sample is not known. Just
Page 4 of 57
