# Difference Between Probability and Non Probability Sampling

*Sampling is a crucial aspect of research methodology, influencing the accuracy and reliability of results. There are two types of sampling - probability and non-probability sampling. The main difference between probability and non-probability sampling is that probability sampling gives every member of the population an equal chance of being selected, while non-probability sampling does not. Learn more about probability and non probability sampling, their types, and the difference between probability and non probability sampling.*

Sampling is a statistical technique where the researchers take a predetermined number of observations from a larger population. The sampling or selection of the sample used for the study is intended for the sample to be sufficiently representative to know its characteristics and analyze the information. So, sampling cuts back when the population is too large, and all its members cannot be surveyed. Sampling aims to obtain information from a smaller group representing the entire population. Sampling methods are divided into two types: **probability sampling** and **non**–**probability sampling**. The article defines the two sampling techniques, their types, and the difference between probability and non-probability sampling.

*Must Explore – Statistics for Data Science Courses*

**Probability Sampling**

Probability sampling is a sampling method that uses random selection methods. The essential characteristic of probability sampling is that everyone in a population has an equal chance of selection. The probability sampling method allows you to create a representative population sample.

For example, in a population of 100, each person would have a 1 in 100 chance of being selected.

Probability sampling uses statistical theory. It randomly selects a small group of people from a large existing population and then predicts that all the answers together will match the population.

*Must Read – Introduction to Probability*

**Non-Probability Sampling**

Non-probability sampling is a technique where a sampler selects samples based on **subjective judgment** rather than random selection. Unlike in probability sampling, where everyone in a population has a chance of getting selected, in non-probability sampling, not all population members can participate.

Non-probability sampling is advantageous in exploratory studies such as the pilot survey (a survey implemented on a smaller sample compared to the default sample size). It is used where it is impossible to draw a random probability sample due to time or cost considerations. Non-probability sampling is a less stringent method. This sampling method is highly dependent on the experience of the researchers.

Non-probability sampling is commonly carried out using observational methods and is widely used in qualitative research.

*Your Career Awaits: Discover the **Best Government Job-Oriented Courses After 10** & **Online Government Certification** Opportunities*

**Types of Probability Sampling**

The types of probabilistic sampling are as follows –

**Simple Random Sampling**

It is a random and automated method to select a sample. This sampling method assigns numbers to the individuals and then randomly chooses numbers. The selected members are then included in the sample.

The samples are chosen in two ways: Through a lottery system and random number generation software. This sampling technique generally works in large populations and has both advantages and disadvantages.

**Stratified Sampling **

This is a method in which a large population is divided into two smaller groups, which usually do not overlap but represent the entire population. These groups can be organized during sampling, and each group can be sampled separately after sampling.

In this method, the samples are classified and analyzed by gender, age, ethnicity, etc. Stratified Sampling divides subjects into mutually exclusive groups and uses simple random sampling to select group members.

**Cluster Sampling **

Cluster Sampling is a method that randomly selects participants when they are geographically dispersed. For example, we have 1000 participants from the entire population of Bangalore. Let’s assume obtaining a complete list of all these is impossible. But instead, what the researcher does is select areas at random (i.e., localities, societies, etc.) and select randomly within those boundaries.

Cluster sampling usually analyzes a population in which the sample consists of several elements, for example, city, family, university, etc.

**Systematic Sampling**

Systematic sampling is a comprehensive implementation of the same probability technique in which each group member is selected at regular periods to form a sample. When this sampling method is used, there is an equal chance that each member of a population will be selected.

*Also **Read – Conditional Probability*

**Types of Non-Probability Sampling**

The types of non-probabilistic sampling are as follows –

**Quota Sampling**

Also called “accidental.” It is generally established based on a good knowledge of the strata of the population and the most “representative” or “adequate” individuals. Therefore, it is similar to stratified random sampling but does not have the random nature of the former.

In this type of sampling, “quotas” consist of several individuals who meet certain conditions, for example, 20 individuals between the ages of 25 and 40, females, and residents in New Delhi. Once the quota is determined, the first ones found to meet these characteristics are chosen. This method is widely used in opinion polls.

**Opinion Sampling**

This type of sampling is characterized by a deliberate effort to obtain “representative” samples by including supposedly specific groups. Its use is widespread in pre-election polls of areas that have marked voting trends in the previous voting. That is, the result of the elections in that area was the same as the overall result.

**Snowball Sampling**

Some elements in the universe lead to others, which then lead to others until a sufficient sample is obtained, completing the census of the universe. Although it may seem useless, it is frequently used when we know the population, for example, with populations such as students, criminals, and certain types of diseases, among others.

**Discretionary Sampling**

Discretionary sampling is more commonly known as purposive sampling. In this type of sampling, the subjects are chosen to be part of the sample with a specific objective. With discretionary sampling, the researcher believes that some subjects are more suitable for research than others. For this reason, those are deliberately chosen as subjects.

**Difference Between Probability Sampling and Non-Probability Sampling**

Aspect | Probability Sampling | Non-Probability Sampling |
---|---|---|

Definition | A sampling method in which each member of the population has a known and non-zero chance of being selected. | A sampling method in which the likelihood of each member of the population being selected is unknown or unequal. |

Random Selection | Random selection of samples from the population is a fundamental characteristic. | Random selection may or may not be used. |

Representative Sample | Tends to produce a more representative sample that closely mirrors the population's characteristics. | It may produce a sample that is not fully representative of the population, potentially leading to bias. |

Inference | Results allow for valid statistical inference and generalization to the entire population. | Generalization of the population may be limited, and statistical inference can be challenging. |

Sampling Error | Known and measurable sampling error, which can be quantified and managed through statistical techniques. | Sampling error can be unknown or difficult to quantify accurately. |

Examples | Simple random sampling, stratified sampling, cluster sampling. | Convenience sampling, judgmental sampling, and quota sampling. |

Statistical Significance | Probability-based methods facilitate the assessment of statistical significance and hypothesis testing. | Statistical significance may be challenging to determine due to non-random selection. |

Research Design | Typically used in rigorous research designs, surveys, and experiments. | Often used in exploratory or qualitative research, convenience studies, or when a strict random selection is impractical. |

**Recommended Reads**

## FAQs

**How is the sample chosen in probability sampling?**

In probability sampling, the sample is selected using randomization techniques such as random number generators, random selection from a sampling frame, or stratified sampling.

**How is the sample chosen in non-probability sampling?**

In non-probability sampling, the sample is chosen based on convenience, judgment, or the researcher's subjective criteria. Examples include snowball sampling and purposive sampling.

**Can probability and non-probability sampling methods be used together?**

Researchers use probability and non-probability sampling methods to balance representativeness with practical constraints and research objectives.

**Is non-probability sampling suitable for generalizing findings to a larger population?**

Non-probability sampling is generally unsuitable for generalizing findings to a larger population because the selection process may introduce bias and limit the sample's representativeness.

**About the Author**

Rashmi is a postgraduate in Biotechnology with a flair for research-oriented work and has an experience of over 13 years in content creation and social media handling. She has a diversified writing portfolio and aim... Read Full Bio