Statistical sampling is the process of studying the population by gathering information and analyzing it. Statistical sampling is the basis of great deal of information where the sample space is large. It is used in various fields like psychology, marketing, politics etc.

Sampling is generally applied because the population is very large and and cannot be studied in entirety. The important aspect of sampling is data collection. Different methods are used by researchers to collect samples to be analyzed.

Some popular methods are:

Random sampling
Cluster Sampling
Systematic Sampling
Stratified Sampling
Convenience Sampling

Random Sampling

Random Sampling is the most popular sampling method used for decision making.

In this method, each item of the population has the same probability of being selected for consideration as any other item. Random sampling may be achieved through computers or random number tables created by Random number generators. Normally, a list of random individuals are generated from a central database. Some simpler examples of Random sampling are following,

Examples:

1.) A hat contain ten numbers (0 to 9), Choosing a number out of a hat without seeing in the hat, These is known as a random sample.

2.) A pact of card contain 52 cards,Choosing a card from the pact without seeing it. These is known as a random sample etc.

In Random Sampling, the samples can be chosen with or without replacement.

When the Sampling is done without replacement,

Example: 1) A hat contain ten numbers (0 to 9), we have to choose two number out of a hat without seeing in the hat,

Then the probability of choosing first number is `1/10` then again choosing the another number from hat, then the probability is `1/9` because there are only nine numbers in the hat after choosing the first number. ( This thing happen in Sampling is done without replacement)

When the Sampling is done without replacement,

Example: 1) A hat contain ten numbers (0 to 9), we have to choose two number out of a hat without seeing in the hat,

Then the probability of choosing first number is `1/10` then we keep the chosen number in the hat again, Then we choose the second number so, the probability of choosing second number is also`1/10`.(This thing happen in Sampling is done with replacement)

Cluster Sampling

Cluster sampling is also called Block sampling. In this method, the population is divided into small groups called clusters. This method can be used whenever the population is homogeneous and can be partitioned. A random sample is taken from one or more clusters and analyzed.

Examples: In a company Director wants to know how many employee use company transport. In the company eighty thousands employee is working. so, He first divide company employee ten groups he takes a data of eight thousands employee, among that nine hundred are using their own transport rest other are using company transport, So according to this data the director of the company analyzed that how many are using company transport. so, here we didnot get the proper data. here we get a analyzed data.

Systematic Sampling

Stratified sampling: In this method, we select every nth item from the population is selected as a sample. This involves a random start and choosing at regular intervals.

Examples: The director of a company wants to know that how many employee are using company uniform, so that he select that person whose Employee id fifty, hundred, one hundred fifty, two hundred and so on. So, here we select a sample in a particular way. So this is known as stratified sampling.

After the data is collected the director analysis about whole company employee,and uses it to make generalizations

Stratified Sampling: In this method, the population is divided into subgroups or strata based on mutually exclusive criteria, then Random or Systematic Sampling is done on the subgroups.

Example: Suppose a company have sell a lot of cloth, then the shopkeeper pick a bundle of cloth randomly and analysis it and according to this he assume about the total cloths. ( Here we pitch only one randomly.)

Convenience Sampling: This type of sampling is also called grab sampling or opportunity sampling. A sample is chosen because it is convenient and readily available. This is the most dangerous and unreliable way of sampling.