Sampling is a technique used in Statistical Analysis in which a fixed number of data points are selected from a large dataset. This selection of smaller subsets helps to perform analysis faster and at a low computational cost.

There are different methods of selecting the samples. Some of them are –

Random Sampling

Random sampling is a sample selection technique in which each element of the dataset has an equal probability/chance of getting chosen.

Random sampling is one of the simplest methods of data selection. It is very popular because it is considered to be an unbiased representation of the population. However, there might be a sampling error owing to variation in representation.

Methods like lotteries or random draws are used in random sampling.

Stratified Sampling

Stratified sampling is a method in which the population is divided into separate groups called strata. The probability sample (usually a simple random sample) is drawn from each of these strata.

The stratified sampling technique has several advantages over random sampling. Stratified sampling can reduce the sample size required for a given level of precision, or it may give a higher precision with the same sample size.