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Types of Statistical Sampling

TopSampling is related to the selection of data from a population of data to define characteristics of the whole data or population.

Advantages of sampling are as follows:
  1. Low Cost
  2. Fast Data Collection
  3. Increased Accuracy of Data

Sampling observation measures different properties of the data like weight, quality etc.

Types of statistical sampling are given below:
  1. Random Sampling: In this type of sampling, occurrence of every element of data is equal. Here, data is not grouped or subdivided and it minimizes the chances of biasness. Sometimes, this sampling can give error, because the randomness by which we choose the element from data may not be representing the characteristics of the population of the data. This type of sampling is tedious, when we are applying it on large set of data.
  2. Systematic Sampling: Data is arranged in some form of order ascending or descending. In this type of sampling, we choose a fixed number position element. It is easier than random sampling. The starting point should not be the first element of the data. It should be randomly chosen. It is a type of Probability sampling.
  3. Convenience Sampling: In this sampling, readily sample data is used.
  4. Cluster Sampling: In this type, sampling data is divided into groups or blocks called as cluster. Then, these groups are randomly selected to get the sample.
  5. Stratified Sampling: In this sampling form, data is divided into groups called as strata. It is done by selecting some characteristics of the data. Each strata is defined into a different subgroup. Advantage of using this sampling is that, by making strata, chances of getting accurate sample increases very high. We can also apply different sampling techniques on different strata depending on the characteristics of the data. It is cost effective for large data. It focuses on the defined strata and can ignore the irrelevant data. Disadvantage is that, it is expensive way of sampling.