POPULATION1

Concept of Population and Sample

In statistics, population is the entire set of items from which you draw data for a statistical study. It can be a group of individuals, a set of items, etc. It makes up the data pool for a study.

Population

It includes all the elements from the data set and measurable characteristcs of the population such as mean and standard deviation are known as a parameter.

For example, all people living in India indicates the population of lndia.

Population and sample are represented in Figure 1

Types of Population

· Finite Population

· lnfinite Population

· Existent Population

· Hypothetical Population

Finite Population : The existing population is defined as the population of concrete individuals. In other words, the population whose unit is available in solid form is known as existent population. Examples are books, students etc.

Infinite population :The infinite population is also known as an uncountable population in which the counting of units in the population is not possible. Example of an infinite population is the number of germs in the patient’s body is uncountable.

Existing Population : The existing population is defined as the population of concrete individuals. In other words, the population whose unit is available in solid form is known as existent population. Examples are books, students etc.

Hypothetical Population : The population in which whose unit is not available in solid form is known as the hypothetical population. A population consists of sets of observations, objects etc that are all something in common. In some situations, the populations are only hypothetical. Examples are an outcome of rolling the dice, the outcome of tossing a coin.

Generally, population refers to the people who live in a particular area at a specific time. But in statistics, population refers to data on your study of interest. It can be a group of individuals, objects, events, organizations, etc. You use populations to draw conclusions.

An example of a population would be the entire student body at a school. It would contain all the students who study in that school at the time of data collection. Depending on the problem statement, data from each of these students is collected. An example is the students who speak Hindi among the students of a school.

For the above situation, it is easy to collect data. The population is small and willing to provide data and can be contacted. The data collected will be complete and reliable.

If you had to collect the same data from a larger population, say the entire country of India, it would be impossible to draw reliable conclusions because of geographical and accessibility constraints, not to mention time and resource constraints. A lot of data would be missing or might be unreliable. Furthermore, due to accessibility issues, marginalized tribes or villages might not provide data at all, making the data biased towards certain regions or groups.

Sample

A sample is defined as a smaller and more manageable representation of a larger group. A subset of a larger population that contains characteristics of that population. A sample is used in statistical testing when the population size is too large for all members or observations to be included in the test.

The sample is an unbiased subset of the population that best represents the whole data.

The process of collecting data from a small subsection of the population and then using it to generalize over the entire set is called samlping

Samples are used when :

The population is too large to collect data.
The data collected is not reliable.
The population is hypothetical and is unlimited in size. Take the example of a study that documents the results of a new medical procedure. It is unknown how the procedure will affect people across the globe, so a test group is used to find out how people react to it.

A sample should generally :

Satisfy all different variations present in the population as well as a well-defined selection criterion.
Be utterly unbiased on the properties of the objects being selected.
Be random to choose the objects of study fairly.

Types of Sampling.

· Probability sampling

· Non-probability sampling

Probability Sampling :In probability sampling, the population units cannot be selected at the discretion of the researcher. This can be dealt with following certain procedures which will ensure that every unit of the population consists of one fixed probability being included in the sample. Such a method is also called random sampling.

Some of the techniques used for probability sampling are:

Simple random sampling

Cluster sampling

Stratified Sampling

Disproportionate sampling

Proportionate sampling

Optimum allocation stratified sampling

Multi-stage sampling

Non Probability Sampling :In non-probability sampling, the population units can be selected at the discretion of the researcher. Those samples will use the human judgements for selecting units and has no theoretical basis for estimating the characteristics of the population.

Some of the techniques used for non-probability sampling are:

Quota sampling

Judgement sampling

Purposive sampling

Comparison of Population and Sample Given Below

Comparison	Population	Sample
Meaning	Collection of all units or elements that possess common characteristics	A subgroup of the members of the population
Includes	Each and every element of a group	Only includes a handful of units of population
Characteristics	Parameter	Statistic
Data Collection	Complete enumeration or census	Sampling or sample survey
Focus	Identification of the characteristics	Making inferences about the population