Most commonly there are two types of sampling processes; probability sampling and non-probability sampling. In probability sampling, the likelihood of a population member being chosen can be calculated, but in non-probability sampling, it cannot be done. For instance, a researcher could be able to determine that one participant has a 35% chance of being chosen to take part in the study, compared to another's 10% chance. However, non-probability sampling ensures that every participant has an equal opportunity to be chosen even when participation is not assured. In this article, we will discuss about non-probability sampling, along with some advantages and disadvantages.

Knowing the kinds of approaches we will employ, will help us be ready when conducting an inquiry and collecting data. Due to this, sampling can be divided into two categories: random or probabilistic sampling and non-probabilistic sampling.

## What is non-probability sampling?

Definition: In a sampling approach known as non-probability sampling, samples are picked or choose by the researcher based on their own evaluation rather than randomly/ random choose. It is a looser strategy. This sampling method heavily depends on the researchers' expertise. Because it is conducted through observation, researchers commonly employ it for qualitative research. Non-probability sampling, in comparison to probability sampling, does not ensure that every person of the population has an equal chance of participating in the study of the researcher. There is a known probability that each person in the population will be chosen. Researchers use this technique in projects where random probability sampling is impractical due to time or cost restrictions.

### Non-probability sampling examples

Here are three straightforward examples of non-probability sampling to help you better comprehend the concept:

1. Using student volunteers who are already familiar to the researcher would be an example of convenience sampling. Students from a specific school, college, or institution can receive the survey from researchers, who can use them as a sample.
2. Technically, the sample chosen at an organisation for examining the career ambitions of five hundred employees should have an equal representation of men and women. Consequently, there ought to be 250 men and 250 women. Since this is unlikely, the researcher uses quota sampling to choose the groups or strata.
3. Researchers also employ this kind of sampling when studying a specific patient illness or a rare disease. To create a representative sample of individuals for the study, researchers can ask participants to provide references for other participants with the same condition.

### When to use Non-probability Sampling?

• Use this kind of sampling to determine whether a population possesses a specific feature or characteristic.
• Researchers that want to conduct exploratory research, pilot studies, or qualitative research typically employ the non-probability sampling method.
• It is used by researchers when they are working under time or financial restrictions.
• The researcher uses this technique when determining whether a specific subject requires in-depth examination.
• Apply it when you do not want outcomes that apply to the entire population as a whole.

1. Probability sampling is not very cost-effective when the population size is quite small. In such circumstances, it is far simpler to merely include sample units at the investigator's choice.
2. In certain situations, it is imperative that certain units be included in the sample. In certain situations, the investigator can use his or her discretion and include these units in the sample.
3. It considers the investigating researcher's expertise, training, and experience. Probability sampling, which is an entirely random procedure, prevents such from happening.

1. This sampling system has a significant flaw in that it is very subjective by nature because the investigator's convenience, beliefs, biases, and prejudices are the only factors that go into choosing the sample. For instance, let us say the researcher wants to carry out a study to find out how much the residents of a certain city make each month. The investigator may deliberately select to include only those persons who live in poorer neighbourhoods and exclude those who reside in nicer neighbourhoods if he wants to show that the city's standard of living has declined.
2. If the sample size is very large, this method cannot be employed since the researcher is unable to personally choose a large number of units in a practical amount of time.
3. An unrepresentative sample may emerge from the investigator making poor decisions due to a lack of experience or subject matter expertise. This could result in inaccurate findings from the study and conclusions.
4. Unlike probability sampling, sample selection does not involve any probabilities, hence it is impossible to estimate the standard error.

## Different Methods of non-probability sampling

Commonly used non-probability sampling methods are-

1. Convenience sampling Method
2. Quota control sampling Method
3. Judgment sampling Method

### 1. Convenience sampling

Careless, unorganised, accidental, or opportunistic sampling are some names for convenience sampling. The sample is chosen based on how convenient it is to use. The researcher chooses certain units that are convenient for him. There is no need to plan when choosing the things. Convenience sampling makes ensuring that the units are accessible and that the source list is readily available. Despite not being scientific, numerous samples are useful for sampling.

A convenience sampling is used in the following situations:

• When the universe is vaguely defined, the sample unit is unclear, and there is not a complete source list available.

### 2. Quota sampling:

Quota sampling combines stratified sampling and purposive sampling elements. In quota sampling, the field workers only include units in the sample that meet certain predetermined criteria. According on one or more parameters, each field worker is given a certain number of units to include in the shipment. To increase the representativeness of a sample, the field worker may be instructed to call every fourth house and interview one person until the quota is reached...

1. When performing sampling research, quota sampling ensures simplicity of usage.
2. If the respondent declines to comply, another individual who is willing to provide information may take his place.
3. Quota sampling is fast and affordable.
4. The only workable method is quota sampling when the population lacks an appropriate frame.
5. The quota sampling method does not require a lot of time to collect data.

1. Those who are readily available and reachable are interviewed. So, quota sampling affects the potential of gathering useful data.
2. The issue of sample unit selection introduces bias.
3. The interviewer's job cannot be effectively supervised. Therefore, the accuracy of the data cannot be guaranteed.
4. The quota sampling method needs multiple researchers. No two people can possess equal competence. Therefore, the conclusions drawn from the study may not be consistent.

### 3. Judgement Sampling

One of the non-probability sampling techniques is judgement sampling. By choosing a group from the population based on the facts at hand, judgement sampling is done. It involves the group being chosen intuitively based on standards that are thought to be self-evident. Under this procedure, units are included to the sample based on the determination that they meet the criteria for inclusion as population representations.

The following are the main benefits of the judgement sampling:

1. Judgement sampling eliminates the expense and effort associated with sample preparation.
2. By using the judgement sampling approach, the researcher can incorporate the advantages of stratification in the sample.

1. Uncontrolled variability and bias are present in the judgement sampling estimates.
2. The only prerequisites for the success of the judgement sampling strategy are eliminating the use of inferential parametric statistical techniques for the purpose of generalisation and having a solid understanding of the population.
3. It is risky to base judgement sampling decisions exclusively on gut feelings and intuition.

## What is the difference between Probability and Non-probability Sampling?

A population is sampled, and research participants are chosen using both probability sampling and non-probability sampling procedures. However, the processing of the two different sampling procedures varies. As a starting point, you should be aware that probability sampling does not give everyone in a population an equal chance of being chosen for a study, whereas non-probability sampling does. As opposed to non-probability sampling, probability sampling can be estimated. Not everyone has a chance to be chosen, even though everyone has a chance of participating. In probability sampling, you can compute the likelihood that a member will be chosen. Additionally, probability sampling relies on random selection, whereas non-probability sampling relies on the researcher's judgement, which may be subjective. When a researcher seeks to eliminate sample bias, they utilise probability sampling, whereas non-probability sampling does not take sampling bias into account. While probability sampling works best when the population's features are different, non-probability sampling is best used when the population's attributes are similar. Finally, since they share characteristics, it is simpler to recruit participants for a non-probability sampling. However, because a probability sampling must be diverse, it is not always simple to select suitable volunteers.