The main goal of any marketing or statistical research is to provide high-quality results that provide a reliable basis for decision-making. Hence the different ones**Types of Sampling Procedures**and techniques play a crucial role in research methodology and statistics.

Your sample is one of the main factors that will determine if your results are accurate. If you conduct your research with the wrong sample designs, you will almost certainly get many misleading results.

On this page you will learn:

- What is sampling?
- The different types of sampling procedures: briefly explained.

Probabilistic and non-probabilistic sampling.

- Infographic as PDF.

**What is sampling?**

By definition, sampling is a statistical process in which researchers choose the type of sample to be sampled. The key here is to choose a good sample.

**What is a population?**

In terms of sampling, a population is a set of units that we are interested in studying. These units must have at least one characteristic in common. Entities can be persons, cases (organizations, institutions) and data (e.g. customer transactions).

**What is a sample?**

A sample is a portion of the population that is the subject of study and is used to represent the entire population. The key here is to examine a sample that provides a true picture of the entire group. It is often not possible to reach all population groups. Therefore, a sample is only examined when conducting statistical or market research.

Full**two basic types of sampling**:

- probability sample
- Non-probabilistic sampling

**probability sample**

What is probability sampling?

In simple terms, probability sampling (aka random sampling or random sampling) uses random sampling techniques and principles to create a sample. This type of sampling gives all members of a population an equal chance of being selected.

For example, if we have a population of 100 people, each person has a 1 in 100 chance of being selected for the sample.

**Advantages of Probability Sampling**:

- A comparatively simpler sampling method
- lower judgment
- High reliability of survey results.
- High accuracy in estimating sampling error
- This can be done even by non-technical people.
- The lack of systematic and sampling bias.

**Disadvantages:**

- monotone work
- Opportunities to select only a specific sample class
- greater complexity
- It can get more expensive and time consuming.

**Types of Probability Sampling**

**simple random sample**

This is the purest and clearest probabilistic sampling design and strategy. It's also the most popular way to select a swatch because it creates swatches that are very appealing**very representative of the population**.

Simple randomness is a completely random subject selection technique. All you, as a researcher, have to do is ensure that all individuals in the population are included in the list, and then randomly select the required number of individuals.

This method gives a very reasonable judgment as it excludes units that come one after the other. Simple random sampling prevents consecutive dates from occurring at the same time.

**Stratified random selection**

A stratified random sample is a population sample that includes the**Division of a population into smaller groups**, called "layers". Then the researcher randomly selects the last elements proportionally from the different layers.

This means that the stratified sampling method is very appropriate when the population is heterogeneous. Stratified sampling is a valuable sampling method because it captures key characteristics of the population in the sample.

In addition, the stratified sample design leads to higher statistical efficiency. Each layer (group) is very homogeneous, but all layers are heterogeneous (distinct), reducing internal variability. A higher level of accuracy can thus be achieved with the same sample size.

**systematic sampling**

This method is suitable when we have a complete list of samples**Topics arranged in a systematic order**such as geographic and alphabetical order.

The systematic sample design process usually involves first selecting a starting point in the population and then making subsequent observations using a constant interval between the samples drawn.

This interval, called the sampling interval, is calculated by dividing the total population size by the desired sample size.

For example, if you were a researcher wanting to create a systematic sample of 1,000 workers in a company with a population of 10,000, you would select all 10 people from the list of all workers.

**Cluster Random Sampling**

This is one of the most popular types of sampling, where members are randomly selected from a very large list.

A typical example is when a researcher wants to select 1,000 individuals from the entire population of the United States, since it is impossible to obtain a complete list of all individuals. The researcher then randomly selects areas (e.g. cities) and randomly selects within those boundaries.

A cluster sampling design is used**when there are natural groups in a population**. The overall population is divided into clusters (groups) and then random samples are drawn from each group.

Cluster sampling is a very typical method formarket research. It is used when you cannot get information about the entire population, but you can get information about groups.

Cluster sampling requires heterogeneity within clusters and homogeneity across clusters. Each cluster should be a small representation of the entire population.

**Non-probabilistic sampling**

The main difference between non-probability sampling and probability sampling is that the former does not involve random selection. So let's look at the definition.

**What is non-probabilistic sampling?**

Non-probabilistic sampling is a group of sampling techniques in which samples are collected in such a way that not all units in the population have an equal chance of being selected. Probability sampling does not imply random selection.

**For example**, a member of a population can have a 10% chance of being selected. Another member might have a 50 percent chance of being selected.

Typically, units in a non-probabilistic sample are selected based on their accessibility. You may also be selected as a researcher through your conscious personal judgment.

**Advantages of non-probabilistic sampling**:

- If a respondent refuses to participate, they can be replaced by another person who wishes to provide information.
- Less expensive
- Very inexpensive and time-saving.
- Types of easy-to-use sampling techniques.

**Disadvantages****non-probabilistic sampling**:

- The researcher interviews people who are easily accessible and available. This means the opportunity to collect valuable data is reduced.
- Impossible to assess how well the researcher represents the population.
- Over-reliance on judgement.
- Researchers cannot calculate margins of error.
- Bias arises when sampling units are selected.
- The accuracy of the data is less certain.
- It focuses on simplicity rather than effectiveness.

**Types of non-probabilistic sampling methods**

There are many types of non-probabilistic sampling techniques and designs, but here we will list some of the most popular ones.

**Convenience-Sampling**

As the name suggests, this method consists of collecting**Units that are more accessible**: Your local school, mall, nearest church, etc. Make a random sample. It is commonly referred to as a sloppy and unsystematic sampling method.

Respondents are those "available for interview". For example, people standing on the street, fans of a brand on Facebook, etc.

This technique is known to be one of the easiest, cheapest and most time-consuming sampling methods.

**Quota Sampling**

The quota sampling method aims to create a sample in which there are groups (e.g. male vs. female workers).**proportional to population**.

The population is divided into groups (also called strata) and samples are taken from each group to meet a quota.

For example, if your population is 40% female and 60% male, your sample should consist of these percentages.

Quota sampling is usually done to ensure that a specific segment of the population is present.

**sampling**

Judgment sampling is a sampling method in which the researcher selects sampling units.**based on your knowledge**This type of sampling is also known as intentional sampling or authorized sampling.

In this method, sample units are selected based on professional judgment that the units have the necessary characteristics to be representative of the population.

Accordinglyhttps://explorable.com/"The process involves nothing more than the conscious selection of individuals from the population based on the knowledge and judgment of the agency or investigator."

Experimental sampling design is mainly used when a limited number of individuals exhibit the characteristics of interest. It's a common way to gather information from a very specific group of people.

**snowball rehearsals**

Snowball sampling is not a common type of sampling method, but it is still valuable in certain cases.

It is a methodology in which the researcher**Recruit others to the study**. This method is only used when the population is very difficult to reach.

This includes, for example, population groups such as working prostitutes, current heroin users, people with drug addictions, etc. The main disadvantage of a snowball sample is that it is not very representative of the population.

**Diploma**

Sampling can be a confusing activity for marketing managers conducting research projects.

Knowing and understanding some basic information about different types of sampling techniques and designs can help you understand their pros and cons.

The two main sampling methods (probabilistic sampling and non-probabilistic sampling) have their specific place in the research industry.

In the real world of research, official marketing and statistical agencies prefer probability-based sampling. While probability-based sampling is always good, sometimes other factors such as cost, time, and availability need to be considered.

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