In statistics as well as in quantitative methodology, the set of data are collected and selected from a statistical population with the help of some defined procedures. There are two different types of data sets namely, population and sample. Let us take a look of population and sample in detail.
Population
It includes all the elements from the data set and measurable characteristics of the population such as mean and standard deviation are known as a parameter. There are different types of population. They are:
Finite Population
The finite population is also known as a countable population in which the population can be counted. In other words, it is defined as the population of all the individuals or objects that are finite. For statistical analysis, the finite population is more advantageous than the infinite population. Examples of finite populations are employees of a company, potential consumer in a market.
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 patientâ€™s body is uncountable.
Existent 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.
Sample
It includes one or more observations that are drawn from the population and the measurable characteristic of a sample is a statistic. Sampling is the process of selecting the sample from the population. Basically, there are two types of sampling. One is probability sampling and nonprobability 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
 Multistage sampling
Non Probability Sampling
In nonprobability 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 nonprobability sampling are
 Quota sampling
 Judgement sampling
 Purposive sampling
Difference between Population and Sample
Some of the key differences between population and sample are clearly given below:
Comparison 
Population 
Sample 
Meaning 
Collection of all the 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 on 
Identification of the characteristics 
Making inferences about the population 
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