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.

## 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 patient’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 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

## 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