Non-Probability Distribution
By: Megha Singhal
Tags: Machine Learning, Artificial Intelligence, Statistics, Deep Learning, Probability

In previous blog we covered probability distribution and its types, now we proceed to Non-Probability distribution and its types.

Non-probability sampling is a sampling technique where the odds of any member being selected for a sample cannot be calculated.

Non-probability sampling is defined as a sampling technique in which the researcher selects samples based on the subjective judgment of the researcher rather than random selection.

Types of Non-Probability Sampling:

a)Convenience Sampling :

Convenience sampling which is also known as availability sampling is a specific type of non-probability sampling method. The sample is taken from a group of people easy to contact or to reach. For example, standing at a mall or a grocery store and asking people to answer questions would be an example of a convenience sample.

The relative cost and time required to carry out a convenience sample are small in comparison to probability sampling techniques. This enables you to achieve the sample size you want in a relatively fast and inexpensive way.Limitations include data bias and generating inaccurate parameters. Perhaps the biggest problem with convenience sampling is dependence. Dependent means that the sample items are all connected to each other in some way.

b)Judgement Sampling:

Judgment sampling is a common non-probability method. It is also called as purposive method. The researcher selects the sample based on judgment. This is usually and extension of convenience sampling.

Judgment sampling may be used for a variety of reasons.

In general, the goal of judgment sampling is to deliberately select units (e.g., individual people, events, objects) that are best suited to enable researchers to address their research questions. This is often done when the population of interest is very small, or desired characteristics of units are very rare, making probabilistic sampling infeasible.

c)Quota Sampling:

A sampling method of gathering representative data from a group. As opposed to random sampling, quota sampling requires that representative individuals are chosen out of a specific subgroup. For example, a researcher might ask for a sample of 50 females, or 50 individuals between the ages of 32–42.

Quota sampling is used when the company is short of time or the budget of the person who is researching on the topic is limited. Quota sampling can also be used at times when detailed accuracy is not important. To create a quota sample, knowledge about the population and the objective should be well understood.

d) Snowball Sampling :

As described in Leo Goodman’s (2011) comment, snowball sampling was developed by Coleman (1958–1959) and Goodman (1961) as a means for studying the structure of social networks.

Snowball sampling (or chain sampling, chain-referral sampling, referral sampling) is a nonprobability sampling technique where existing study subjects recruit future subjects from among their acquaintances.Snowball sampling analysis is conducted once the respondents submit their feedback and opinions.Used where potential participants are hard to find.

Advantages of Snowball Sampling

The chain referral process allows the researcher to reach populations that are difficult to sample when using other sampling methods. The process is cheap, simple and cost-efficient. This sampling technique needs little planning and fewer workforce compared to other sampling techniques.

Disadvantages of Snowball Sampling

The researcher has little control over the sampling method.

Representativeness of the sample is not guaranteed.

Sampling bias is also a fear of researchers when using this sampling technique.

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