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SIMPLY Statistics: refers to averages, analysis of data, study of principles and applied methods, and interpretation of enquired data.

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Key Points

Statistics is very important to analyse the data clearly. It helps to plan on your own based on the collected data.
Modern economics has included the study of statistics to study the information and facts in more quantitative and scientific manner (systematic procedure). That is called as the data.
Prof. Ronald A. Fisher is considered as the father of statistics because of his contribution towards development of theories and experiments in statistics. that led to involvement of statistics in various subjects like psychology, biology, marketing/advertisements, sociology etc.
Statistics is the science as well as an art.
when we study the data, we may explain all its causes and factors theoretically. but, when we understand and measure the data clearly, it's very helpful to plan policies to solve various economic problems such as unemployment, poverty etc.
Qualitative information is collected and arranged systematically in economics to study about a particular object and its relationship with other ones. it is also used to describe its degrees or levels such as worst/good/better or skilled/unskilled/... healthy/excellent etc.
quantitative information describes about an object numerically as level of income, prices, cost of production, level of consumption, savings, investments etc...
Statistics enables an economist to present data more clearly and it is valid forever rather than theoretical statements.
Statistics also enables to explain the huge / mass data into smaller quantitative measures such as mean, median, mode etc. (averages)
It is used to find the relation between different variables / factors such as prices, demand, supply etc. we can understand relation more clearly.
Statistics helps the economists and other analyzers to valuate the statements given by the researchers whether true or falls.
It is also used in predicting future based on current data.
In India the statistics is already in use before some thousands of years ago in the books of Shukra-Niti and Manusmriti.
There is a tremendous growth in statistics due to an increase in demand for statistics and low cost of implementation and maintaining data.

Econometrics

Using statistics and mathematics in economics is called as econometrics. It is a new branch in the subject of Economics. It is the combination of economics statistics, mathematics, Economic theories and statistics.

explaining theoretical economic models into some useful source or tools for policy making in more quantitative way. The main objective of econometrics is to convert qualitative statements (sentences/words) into quantitative statements (numbers/numerical). Econometrics is used in many fields of economics such as Finance, labour, Macro, Micro and policy making. almost many decisions regarding policies and programs/schemes are made using econometrics as well as to understand its impact.

Statistics vs econometrics

Both statistics and econometrics deals with the data. Statistics is part of applied mathematics (Refers to the application of maths in different areas). whereas, Econometrics is one of the branch in economics. statistics deals with high level of study broadly but, econometrics uses statistics but not broad as statistics. Statistics includes only a particular field of subject and econometrics includes the theories in economics, maths and statistics.

Why to study statistics in economics

Economics is an interesting subject which is everywhere right now. The main aim of economics is to solve economic evils/problems/issues like unemployment, income inequalities, poverty, etc. an organization needs some data to solve a particular problem to take better decisions and to implement. that required data is collected using various methods in statistics.

Without statistics many questions will not have answer in economics like:

Living standards of the people
rate of economic growth and development
distribution of national income
value of per capita income and
ability to pay taxes. etc.

Meaning and definition of statistics

The word "statistics" is derived from Latin and Greek words "status" which refers to a political state. It is also derived from an Italian word called "Stato".

The word "statistics" is used in two ways:

Singular: Singular means collection, classification and presentation of the data. It is systematic and scientific in nature.
plural: It refers to all the statistics as a single data. (usage of the data). In the plural sense, it refers to the numerical facts and figures systematically collected for some special purpose.

Definitions

the definitions in statistics are classified into 2 categories. They're:

In Plural Sense,
In Singular Sense.

According to Tate, “You can compute statistics by statistics from statistics.”

Following definitions are in plural sense.

According to L. R. Connor

”Statistics are measurements, enumeration or estimater of natural or
social phenomena systematically arranged so as to exhibit their interrelations.”

* According to A. L. Bowley

Statistics are numerical statement of facts in any department of
enquiry placed in relation to each other.

Following definitions are in singular sense.

According to Croxton and Cowden,

Statistics may be defined as the collection, presentation, analysis
and interpretation of numerical data.

According to A. L. Bowley,

Statistics
is the science of measurement of social organism, regarded as a whole in all its manifestations.

According to Seligman,

Statistics is the science which deals with the methods of collecting, classifying,
presenting, comparing and interpreting of numerical data collected to throw some light on any sphere of
enquiry.

Importance or (Significance( of statistics

Statistics enables a person to interpret the data by using various methods. Conducting of statistics paper in examination of different courses like Charted accountant, cost accountants, economics, natural sciences, psychology, etc has become mandatory because of its importance and applicability. we can study the flow of income and the level of savings and consumption, level of tax payment in a country. It is also important in business management, commerce/marketing, planning, research (Public opinions/surveys) etc.

Importance of statistics in Economics is as follows:

Quantitative expression of economic problems: it simply refers to the way of expressing the data. in economics some problems cannot be expressed clearly in theoretical statements but, they're expressed more by quantitative approach. An economist may better understand the problem of poverty by comparing the ratios of different years numerically (quantitative) rather than qualitative data.
Comparisons: Quantitative data is not only useful to better understanding but also it is very useful to compare the progress of an economy. Inter-sectoral comparison refers to the comparing between different sectors. Inter-temporal comparison refers to the comparing the progress in different periods/times and planning years.
Causes and effect relationship: statistics is useful to understand the causes of a particular economic problem and along with its effects to the economy. finally, it is very useful to understand the problem better to implement right policies.
Economic equilibrium: The term equilibrium refers to the state of balance or stability or state of changelessness. statistics provides the information about the behaviour of both the producers and the consumers. it specifies the combination of inputs, amount of output, consumer choices etc. it helps producers to take better decisions and economists to work for market equilibrium.
Economic theories: By using different methods in statistics, Economists can develop different theories and models to explain about a particular factor in relation to many variables. for example, changes in prices of basic mobiles by the introduction of smart phones and its increasing demand. Hence, it is very useful to draw many conclusions as well as to develop new theories /economic models.
Forecasting: Forecasting refers to the historical data as inputs that are used to predict the future trends and directions. It is a technique to expect future causes. It enables the economists to understand the changes in different factors which effects other factors and finally that leads to instability. The statistical data provides required information to well predict the future cause and enables economists and analysts to take suitable measures to overcome the problem.
Policy making: Statistics is very very important to implement or modify/change the existing policies. for example, if government wants to introduce a policy regarding to decrease unemployment rate, it requires the data of number of unemployed students, their skills and abilities, age, personal details etc. It is even required to alter/change the existing policies also.
Important to state: for state administration purpose the state requires some accurate data or information. It may be about national income, budget/finance, transportation or markets or population. Hence it plays a key role in state administration.
Important to research: many universities and organizations conduct research to understand public interests and latest data in many fields of subjects. Research is very important to get new ideas and to take better decisions. the methods in statistics for collecting data helps the individuals and groups to collect data in various methods by allocating required time and resources.
important to manage business and its decision process

Once listen to me!

Practically I tried to explain the importance or significance of statistics in economics as well as in the real world. Hope surely its clear to you. Now we will look at some limitations or restrictions of statistics. It also has few barriers in maintaining and computing data.

limitations of statistics (Restrictions / barriers):

As we seen above, statistics is used almost in every subject in different fields and in many human activities. It is used highly in social sciences, physical and life sciences for research. because subject may be different but, research is same. However it also has many drawbacks and restrictions. they are:

Deals with only averages: Statistics doesn't provide the individual details of figures. according to Prof. A. L. Bowley, "Statistics is a science of average". For example, the average performance of II Year students in economics examination is 75%, then the students who studies better are equal to those who studies rarely. so we can say that averages are just values to analyse and predict only.
Absence of uniformity: It means the data obtained from statistics is not same i.e. it is always heterogeneous. the data which is not similar has no value for the purpose of collecting data. Hence, sometimes the data which is not similar is considered as invalid.
Statistics require only homogeneous data.
Statistics can only be used by the experts.
Does not study individuals: Generally statistics deals with averages of the data. so, it does not deal with the values of individual figures even they are very important. For example, Economics marks of X student (Individual) is not subject matter of statistics but, average marks of some students are valid in statistics. hence it is suitable for only averages than individual facts.
Higher possibility to misuse: Statistics provides large amount of data and its computation. It requires a well skilled person who has good knowledge and genuine, honest and uncorrupted person to deal with statistics. otherwise the person may draw wrong conclusions in his favour and may use falls statistics. Hence it is also one of the limitation of statistics. "In the words of W. I. King, “One of the shortcomings of statistics is that they do not, bear on their face the label of their quality.”
Statistics is not applicable to qualitative data: Qualitative data refers to that data which is in the form of descriptive text. It is also collected using various methods like interviews and questionnaires. Statistics only deals with quantitative data (Numerical ... Numbers) but not with qualitative data. so, it is not useful to measure degrees of qualitative attributes like good, bad, satisfied, healthy, etc.
Ignorance in terms of qualitative data: The data which cannot be expressed in terms of quantitative I.E. numbers/numerical is not studied as part of statistics. It is possible when qualitative data is converted into quantitative data. Now a days research is going on about measuring qualitative response into quantitative data. The best example is that, Psychology uses various methods to record human reactions in terms of quantitative data by using various standardize devices. Human reactions are qualitative in nature.
cannot make clear conclusions: Statistics provides large amount of data. but when the data of an occurrence or a phenomenon is available, it cannot provide the causes or other qualitative data. hence, our conclusion which is drawn from the statistical data is not clear and accurate because, it ignores other side of qualitative data which contain some information about the phenomenon. so, qualitative data is also very important.
Availability of many methods to solve problem: there are many methods to solve the same problem in statistics. finally, using different methods to get same result leads to variations in the results. for example, there are mainly 3 methods to perform deviation.

Stages or (Characteristics) in statistics

Collection: We all know that collection is the first step in collecting or enquiring about the data. data should be collected clearly and carefully because, if the data is not proper, then the conclusions are also falls one. that is why collection is the backbone of statistical data or enquiry. If the data is collected for the first time or (Primary data), it is a challenging task to the investigator or the researcher. So, proper collection of data is very very important.
Organisation: after collection organisation is the next step in statistics. generally if the data is collected from secondary sources or published sources, then it is in proper organised form. but in case if the data is collected directly from the research; it has to be organised properly. the main step in organising data is editing i.e. correcting the errors and wrongly counted calculations. after editing, the final step is Tabulation (It means arrangement of data in rows and columns clearly). so organisation of the data involves mainly: editing, classification and tabulation.
Presentation: presentation of the data is another important step in statistics. data should be presented properly to provide clarity on the figures. proper presentation of the data enables the analyst to understand and analyse the data clearly.
Analysis (Study / evaluation): after completing the process of collection, organising, tabulation and presentation of the data; The next step is analysing the data. analysing means drawing a clear conclusion and defining. with the available data, the researcher uses many methods to understand and analyse the data (averages). there are mainly Scientific, Numerical and empirical analysis.
Interpretation(explanation): It is the final step in the statistics. It refers to commenting or drawing conclusions on the analysed data. Interpretation is not a simple task. it requires a specialised, skilful, experienced professional. If the data is not properly interpreted or concluded, the whole process of collecting and analysing data becomes useless.

From the above stages, you can easily define the term statistics.

Statistical survey

Statistical survey or statistical enquiry refers to search for knowledge with the help of statistical methods. It is also very practical job which requires a skilled person. it also has few points while planning for statistical survey. Proper execution of plan is mandatory while conducting statistical survey to get clear result without wasting time and resources.

Statistical units

It means the measurement of the collected data. Statistical units are the units of the collected data. for example, Person's weight in Kilograms (KG), measurement of land in hectares and acres, Cup of coffee, etc. Simple, measurement of data in terms of anything is a statistical unit.

Statistical data or (Data)

Data refers to economic facts which is in terms of numbers is called as the data. numbers means quantitative (Numerical). Simply in other words data means A sequence of observation, made on a set of objects included in the sample drawn

from population is known as statistical data.

Ungrouped data

It is the primary data which we collect directly. it is generally in the form of numbers. it is also called as raw data. It is not classified or presented.

Grouped data

Data presented in the form of frequency distribution is

called grouped data. it is the process of aggregating individual observation of a variable into groups.

Frequency distribution

In statistics, it refers to the tabular or graphical representation of the data which is expressed on the range of the class interval or observations. in a frequency distribution, the raw data is expressed into groups known as classes. so, the number of observations that fall in each class is known as frequency. hence, a frequency distribution has 2 parts.

* The Left part refers to the class interval.

* The right part refers to the frequencies.

Diagram

diagram is a visual presentation of statistical information. It leads to proper understanding of the data analysis.

Graphical presentation

graphical presentation refers to the way of presentation of the data by using graphs. graphical presentation is mostly useful in data presentation related to time series. Frequency distribution is possible even by using graphs.

Types of diagrams

Generally diagrams are used to present the statistical data effectively. Following are the some of its types which are used widely to express qualitative data.

One-dimensional diagrams

Here the basis of comparing data is linear or one-dimensional in nature. It is a simple bar chart diagram with large number of variations. it is used to present and compare the magnitude or size of the co-ordinate items of the data. For example, line diagram and bar diagram, simple bar diagram, multiple bar diagram, subdivided bar diagram, percentage bar diagram, etc.

Two dimensional diagram or (2D)

They are constructed according to the length and width of the given data. for example, squares, circles, rectangles, etc.

Three-dimensional diagrams (3D)

These diagrams are also known as the volume diagrams. these diagrams are constructed according to Length, width and height of the given data. These are useful in situations for better understanding. For Example, Cubes, prisms, pyramids, spheres, cones, and cylinders

The main difference between two-dimensional diagram and 3-dimensional diagram is

2D diagrams are constructed according to length and width only. whereas, 3d diagrams are constructed according to length, width and height.

Pictograms

Pictograms are used to present the statistical data in the form of pictures. It is one of the type of diagrammatic presentation using pictures.

Cartograms

Cartograms simply refers to a map which shows geographic statistical data by using shades, curves and dots. Cartograms present quantitative knowledge of geographical statistical data.

Sources of data or (Types of data)

we now look into the sources of the data. that means what are the main sources of the data. because in general way data classification is done based on the sources of data but not on types of data.

Primary data: data collected by an investigator or researcher for a particular purpose. the investigator may be a student, teacher or other person but, has a clear purpose for collecting data.
Secondary data: refers to the data which is already collected by a person for a purpose but used by the researcher for another purpose. It is already published data.

Other classification of the data

Internal data: internal data means data from within the area or a particular business.
External data: external data means the data collected from outside sources. It may be collected from primary or secondary data.

Primary data (Original)

Primary data is first hand information collected directly. It is directly collected by the investigators or researchers from the fields of their study. It is original data forever. it is also known as the raw data. It is collected for their own uses or purposes. the researcher himself involves in the collection of the data or supervises others under his management. so this is called as primary data. it may be collected by using any of the methods like interview, questionnaire, senses or case studies. Primary data is collected when there is no statistical data available or when we require fresh data. Data collected by NSSO and CSO are some examples.

For example, In My college I.E. BJR GDC collection of student details such as their course, Year, combination, personal details etc are collected from each student for college purpose. It's best example of primary data. Our college used questionnaire method during lockdown (Covid19) period to collect data. they used google forms to record responses by the students.. Here there is a need to collect data because it is not available with our college.

Note: the researcher may use any of the methods according to the need and importance of getting data.

Some advantages are as follows

researcher or investigator has clear purpose of collecting the data.
High rate of accuracy. it is very quality data which is obtained directly from the population.
he can get additional data if he wants during analysis.
They are reliable.
they are original.
this data is latest and fresh I.e. not outdated.

Some disadvantages or demerits as follows

Its time consuming or time taking.
Skill: requires a skilful person.
Funds: Not easy to get funds for investigation sometimes.
the data assumed as unnecessary or useless is not considered. it's ignored.
involves large number of investigators or labours.
chance of personal bias and prejudices.
if the participant is not active, reduces the quality of the data.

Secondary data (Existing data)

secondary data refers to the second-hand data. It means the data which is already collected and used by some organisation or someone for a purpose and reused for another purpose. It is also known as finished data of investigation. there are number of sources for secondary data such as radio stations, news papers, government published information, blogs, written diaries, etc. it means the data is already collected by a researcher and used for his purpose and if someone collects that data for his purpose, then it is called as secondary data. if the researcher himself/herself collect the data by research, it is called as primary data.

According to M. M. Blair,

"Secondary data are those already in existence and which have been collected for

some other purpose".

For example, Let us understand this concept from Covid19 data. During Covid19 pandemic, many videos and articles (Data) is published by the media. It is called as secondary data because, media did not collect the data and analysed. but In reality, Health professionals interacted with Coronavirus patients and collected the data. that is primary data. but here media worked on this primary data for its purpose of publishing. and we are just analysing the edited version of the data and truly we have to believe because we always don't have access to primary data. and still we are not truly aware about actual reality.

Merits of secondary data

It saves resources and efforts.
It consumes less time.
less expensive.
accessibility in internet: there is easy access in internet if we want to get any data instead of having membership in libraries, institutions etc.
easy to eliminate errors by the investigator.

demerits of secondary data

no clarity regarding the quality of the data.
required data may not be available.
It may be outdated. (Hence it is not useful to you).
quantity of the data is not appropriate to its purpose. secondary data provides mass information but it may be not related to the purpose of collecting. Generally primary data is collected for a particular aim or purpose. but collected existing data may not satisfy the aim or purpose of collecting the data.

Listen to me

Hope you now clear about what is primary and secondary data. If you are clear; you can surely make difference between these 2 concepts. Find more and more examples of primary and secondary data.

Sources of secondary data

Published sources: refers to the data which is published by national and international agencies. It's secondary.
Unpublished sources: refers to the data which is collected but not published. like government offices records, account maintenance books, CBI records, etc. this data is also secondary data but not published.

Listen to me!

To continue topics, it is required to know some more basics. I will introduce some more basics in this blog to make you clear about concepts. Hope it is useful to you. so that you can understand further concepts clearly.

Classification of the data

Classification is the process of arranging the collected data into classes and to subclasses according to their common characteristics. classification is the process of arranging similar available data into groups or classes according to the needs and purpose of study. generally collected data is heterogeneous (means unrelated or not same) so, by classifying the data into groups, the data achieves homogeneous status (Related or similar).

Why classification is important?

Generally the researcher will have mass data available to him after collecting the data. it is not possible to understand or study the huge data directly. classification of the data enables the investigator or researcher to

compare the data
analyse the data
he can avoid irrelevant or unrelated data.
Drawing logical conclusions is more easy.
classification is very useful for tabulation.
in business and organisations, it specifies which data to be kept confidential.

Types of Classification of data

classifying means arranging data into groups, classes and subclasses based on its similarity.

Following are the methods of data classification:

Qualitative classification: here, data is classified based on the qualitative characteristics or attributes. (in the form of text) like employed, student, profession etc. it is also classified into 2 kinds of classification They are: 1. two-fold (based on presence and absence of attribute eg: married/unmarried, vaccinated / not vaccinated). 2. Manifold classification (classification based on more than 1 attribute. here data is further classified into sub classes and groups).
Quantitative classification: which can be measured in terms of numbers. data is classified based on the measurement of variables. for example, classification of students in terms of marks obtained.
geographical classification: here data is classified based on the place or a particular location. for example, total students received scholarships in Telangana state is xxxxx. in Hyderabad number of students received scholarship amount are xxxx.
Chronological Classification: here data is arranged based on the time. it is also known as time series.

Tabulation of the data

The process of presenting data in the tabular form is termed as tabulation.

There are some rules to arrange the data in tables (tabular form) and a table also have some important parts also.

*Senses and sampling

Simple,

when whole population is considered to collect data that is called as senses method.

whereas, when a small group which represents the total population is considered to collect data, that is known as sampling method.

Senses method or (Senses investigation)

When the researcher or investigator collects data about each and every item in the population is known as senses method. In other words, the method deals with entire population to collect data. that's why it is also called as complete enumeration method. For example, if state government wants to know information about vaccinated students in Hyderabad degree colleges, the investigation involves all the students to provide the data (that is total population because, information from all units is collected belonging to degree college students in Hyderabad). that is called as senses method.

eg: data research conducted by the Registrar General and Census Commissioner of India (population senses)

Merits of senses method

it provides huge amount of key data like literacy rate, birth rate, death rate, Infant mortality rate, etc.
data is more accurate
Each and every item is considered to collect data
reliable
Collected data can be used for other purpose also.

some demerits

very expensive and time consuming
cannot provide urgent information if required immediately.
requires more labours
Chance of errors in collection of data.

Finally: if the survey includes all the population to collect the data, it is known as senses method.

Sample survey method

In statistic research, sample method is another method which is mostly used by many data analyst to collect data from few units of the population. if a survey or investigation is conducted by considering few members or samples of total population, then it is known as sample method. that is why it is also called as portion

enumeration method. so, we can say that a sample may be anything that is less than a full survey of a population. that means sample members who participate in survey are always less than the total population. Sample survey means collecting information from a sample. for example, when we go to a hospital for blood test, they will take few drops of blood from your body as a sample and studies the changes in entire body. so, here some units represents whole population.

Suppose, If you are going to a market or a shop to purchase vegetables and commodities, you'll take out a small peace or small part of that commodity and you'll test the quality of that entire commodity like food grains, vegetables, etc.

For example, If a researcher wants to collect data from the students about Reasons for distraction, knowledge of teachers, about the college, active students, lack of participation by girls/boys in the class etc... he will select few students in the class as a sample and follows a particular technique and finally he collects the data. Hence, information about whole class and the college can be studied.

Some merits

Sample survey method is very popular in use today. It has number of advantages when compare to senses method.

Takes less time, money and labour
easy to analyse errors
data acquired is more accurate and reliable.
Easy to draw conclusions about whole population.

However, drawing a sample out of population should be careful.

Some disadvantages

Chances of bias.
Difficulty in selecting a good representative sample.
Unscientific: sample population may not be useful for the researcher and he may not continue systematic process and it becomes unscientific.

Finally: Sample survey means survey of few population or sub set of population rather than whole population. collecting data from selected few people instead of whole population.

Sample, Sampling And Population

* Sample refers to a group of people who takes part in survey. they represent entire population.

* Sampling is the process of selecting participants from the total population.

* Population refers to group which is used to draw the sample from it. In statistics it refers to the totality of the data which we are talking about. For example, total chocolates in the box, total students in BA group, Total number of Economic graduates etc. we can define population in many ways like this.. It may be anything which we consider as a data.

Population

Population is also known as universe.
In statistics, all the items which fall under the survey or enquiry are known as population.
It is the set of all possible observations.

Concepts

Finite population: If the units of the population are measurable, they're called as finite population. for example, number of literates in India, number of government employees in Telangana, etc.
Infinite population: If the units of the population cannot be measurable, they're known as Infinite population. for example, Number of stars in the sky, number of bacteria cells/organisms, etc.
Population size: total number of population is considered as the size of population.

There are 3 types of sampling methods for selecting participants.

Probability Sampling: having equal chance of a sample for being selected.
Non-probability Sampling: selection based on subjective judgments of a researcher rather than random selection.
Mixed sampling: combination of random and non-random sampling methods to select a sample.

Probability sampling (Random sampling)

In probability sampling all population has equal chance of getting selected. that's why it is also called as random sampling. It is based on the theory of probability. here there is no bias in selection of population to draw a sample. In simple words, the selection is done without having a plan.

A small example to make you clear!

A teacher giving her lecture. after completing her lecture, she will now ask some 4 to 5 students randomly without selecting a particular student out of 70 students. So, in this case all 70 students has equal chance of getting selected by the teacher to ask a questions from her delivered lecture. This kind of random selection is known as probability sampling or random sampling.

Types of probability sampling

Following are the types of probability sampling techniques:

Simple random sampling (SRS)

It is a one of the technique where all the units of population which are drawn for sampling has equal chance of getting included independently in the sample.

Stratified sampling

before selection the population is classified into different categories according to the requirements of research. Then each category is selected randomly in the sample. Stratified random sample is better than random sampling but it requires clear knowledge about the population to classify.

Cluster sampling

It is also another method to study the huge population. Here, the population is divided into groups called as clusters. population division must be in heterogeneous groups i.e. there is no classification based on similarities like age, qualification, religion etc. then the researcher selects few groups as a sample.

Remember

Don't confuse! The main difference between stratified sampling and cluster sampling is:

classification: its based on similarities features like age, gender etc. but in cluster sampling, there is no classification based on similarities.
sample formation: population is selected randomly from each group. but in cluster sampling: whole group is selected randomly.

Systematic sampling

also known as quasi-random sampling. this method is used when data is available in computer. here the data is arranged in an order like alphabetical, numerical, etc and selected randomly.

Non-Probability sampling or (Non-Random sampling)

Not every member of the sample has equal chance of getting selected. It is mostly based on the knowledge and judgements of the researcher. that's why the reason it is called as Non-random sampling method. It is always biased in nature. Researchers mostly use this method when there is less time to select members randomly. Selection is depends on a plan. Its members are selected from Choice but not by equal chance.

Example

For example, While taking classes, Mostly teachers will ask questions to the students randomly without selecting a particular student on his choice.. Suppose, if an education officer is visiting that college/school, the teachers select good students as a representation of whole class with a plan and choice. this kind of selection is known as non-random or non-probability sampling. Its biased in nature.

Its types

Following are the types of non-random sampling

Convenience sampling

Members are selected based on the availability of the members and convenience of the researcher.

Consecutive sampling

Quota sampling (reservation)

Here the members are classified into groups like gender, age, religion, poverty, district etc and researcher provides fixed reserve for the classified groups for sample. for example, researcher may create reservation for 50% for males and 30% for females. he compulsorily considers/accepts 50% males and 30% females as sample for his research. It is already planned and reserved but not random selection.

Judgmental or Purposive sampling

Here sample is selected depends on the purpose of study. if the members are beneficial to his research, then they are selected as a sample. If the members are not related to his enquiry, Its no use about conducting research with them. Simple, The researcher selects few members who are really important for his research.

Snowball sampling

It is also another method/technique for non-random sampling. It is used when the members are not easily available or if the researcher finds difficult to find a suitable one for his research. In this process, the researcher finds a person and tells him to refer another person for his research. it keeps on going until the researcher finds right person for his research. When the members are difficult to find, this method is used.

The end

Thanks and regards

A. Harish

* Hope this work is useful to you.

* I thank you for spending your valuable time in this post.

Top thoughts from the students

I don't understand why this textbooks and materials make the content more complicated and hard to understand.
Knowledge and conceptual understanding is important. but why introduce so much of stuff without proper concept in a topic.
There is really fault in the way of learning as well as teaching.
Concept is important. other information is not at all important. but for knowledge purpose, concept should be linked with information. It should be used in daily life.

Statistics For Economics // Econometrics (Concepts In Brief) - Self_Project