 # Types of Sampling Methods

For any statistical analysis of large amounts of data, it is not always possible to evaluate every element. In such cases, several approaches are made to simplify data measurement to cope with limited resources. One of the most common methods used or analysing and measuring data on a large scale is the sampling. There are various types of sampling and sampling methods used in statistical analysis.

What is Sampling?

Sampling is a type of method used in a statistical analysis where a selected number of elements are taken from a comparatively more extensive population. The idea and work process behind taking a sample from a more significant population depends on the type of statistical analysis being conducted.

In simple terms, it is a statistical process that concerns the predetermined elements of a specific data set that facilitates further analysis and inferences about that entire group.

Why is Sampling Important?

In the case of a large population, gathering data about every single element can be time consuming and expensive. A population is defined as a whole or a mass, which involves all elements and their characteristics for studying a particular data set.

With the help of sampling, an arbitrary section of a population is taken as a sample for analysis. It helps analysts to make inferences about an entire population quicker than the manual observation strategy.

So, for statistical analysis of a large population, it is a common practice to take a sample. Thus, sampling makes the study much more efficient and cost-effective, thereby showcasing its importance in statistics. There are different types of sampling techniques, each applying a unique strategy to gain knowledge about a broad set of near about homogeneous elements.

Different Types of Sampling Methods

Sampling methods can be broadly categorised into two types – random or probability sampling methods and non-random or non-probability sampling methods.

Random or probability sampling methods can be further sub-divided into 2 types, i.e. restricted or simple random sampling and unrestricted random sampling. Restricted random sampling can be further classified as systematic sampling, stratified sampling, and cluster sampling.

Meanwhile, non-random or non-probability sampling consists of 3 types that are judgement sampling, quota sampling, and convenience sampling. You can get a clear understanding of the various methods of sampling and its types from the illustration below –

Restricted Random Sampling

• Systematic Sampling

• Stratified Sampling

• Cluster Sampling

Non-Random Sampling

• Judgement Sampling

• Quota Sampling

• Convenience Sampling

1. Random or Probability Sampling

Among the different types of sampling in statistics, random or probability sampling method deserves mention. In the case of random or probability sampling method, every individual element or observation has an equal chance to be selected as samples. In this method, there should be no scope of bias or any pattern when drawing a selected group of elements for observation.

As per the law of statistical regularity, a random or probable sample of an adequate size which has been taken from a large population tends to have the same features and characteristics as those of the entire population as a whole.

Random or Probability sampling can be broken down into 4 types, they are –

1. Unrestricted or Simple Random Sampling

Such type of sampling is done with the random number generator technique.  It is also termed as unrestricted random sampling for its lack of predeterminants in picking a sample from a population.

Thus, simple random sampling is also called unrestricted random sampling. This method has two types of procedures, samples drawn with replacements and without replacements.

1. Systematic Sampling

Systematic sampling falls under the category of restricted random sampling, which means that it is not purely random. Samples are taken when elements meet certain criteria.

In the case of systematic sampling, the entire population is arranged in a specific order. Then, every nth element of that population is selected as a sample. For example, for evaluating the marks in language subjects of all the students of standard 6, every 5th student’s marksheet is selected as a sample. Here, n = 5.

1. Stratified Sampling

In this method of statistical analysis, the whole population is segregated into multiple homogenous groups or strata. From each stratum, samples are picked at random.

For example, if measuring the number of winter clothes with hoodies in a garment store, firstly all clothes might be separated as men’s, women’s, and kids’ and then random hoodies picked from each group act as samples for analysis.

1. Cluster Sampling

For cluster sampling, the whole population is divided into clusters and then selected as samples. These samples are divided multiple times into smaller fractions until the sample size is reduced to a state that is reasonable for statistical analysis. That is why it is also known as multi-stage sampling.

For example, departments of a business can be clusters as well as the number of roads within a city.

1. Non-random or Non-Probability Sampling

In case of a non-probability sample, the elements and observations from a broader population are selected based on non-random criteria. So, each element of a population does not possess equal chances of being in a sample.

However, in the case of such a sample, it is not possible to make a valid judgement on the whole population. Researchers use this kind of sampling method to develop an initial understanding of a small or semi-analysed population. Such methods are mainly of 3 types based on the choice of element selection, which are judgement sampling, quota sampling, and convenience sampling.

Sampling Errors

Sampling error is a type of statistical error, which differentiates the analysis of samples with the actual value of the investigated elements and observation of a population. There are different types of sampling errors, among them the important ones being, biased and non-biased errors. The magnitude of both types of sampling errors can be reduced by drawing a bigger sample.

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