The main goal of any marketing or statistical research is to provide quality results that are a reliable basis for decision-making. That is why the different **types of sampling methods** and techniques have a crucial role in research methodology and statistics.

Your sample is one of the key factors that determine if your findings are accurate. Making the research with the wrong sample designs, you will almost surely get various misleading results.

On this page you will learn:

- What is sampling?
- The various types of sampling methods: briefly explained.

Probability and non-probability sampling.

- Infographic in PDF.

**What is sampling?**

Dy definition, sampling is a statistical process whereby researchers choose the type of the sample. The crucial point here is to choose a good sample.

**What is a population?**

In sampling meaning, a population is a set of units that we are interested in studying. These units should have at least one common characteristic. The units could be people, cases (organizations, institutions), and pieces of data (for example – customer transactions).

**What is a sample?**

A sample is a part of the population that is subject to research and used to represent the entire population as a whole. What is crucial here is to study a sample that provides a true picture of the whole group. Often, it’s not possible to contact every member of the population. So, only a sample is studied when conducting statistical or marketing research.

There are** two basic types of sampling methods**:

- Probability sampling
- Non-probability sampling

**Probability Sampling**

What is probability sampling?

In simple words, probability sampling (also known as random sampling or chance sampling) utilizes random sampling techniques and principles to create a sample. This type of sampling method gives all the members of a population equal chances of being selected.

For example, if we have a population of 100 people, each one of the persons has a chance of 1 out of 100 of being chosen for the sample.

**Advantages of probability sampling**:

- A comparatively easier method of sampling
- Lesser degree of judgment
- High level of reliability of research findings
- High accuracy of sampling error estimation
- Can be done even by non-technical individuals
- The absence of both systematic and sampling bias.

**Disadvantages:**

- Monotonous work
- Chances of selecting specific class of samples only
- Higher complexity
- Can be more expensive and time-consuming.

**Types of Probability Sampling Methods**

**Simple Random Sampling**

This is the purest and the clearest probability sampling design and strategy. It is also the most popular way of a selecting a sample because it creates samples that are very** highly representative of the population**.

Simple random is a fully random technique of selecting subjects. All you need to do as a researcher is ensure that all the individuals of the population are on the list and after that randomly select the needed number of subjects.

This process provides very reasonable judgment as you exclude the units coming consecutively. Simple random sampling avoids the issue of consecutive data to occur simultaneously.

**Stratified Random Sampling**

A stratified random sample is a population sample that involves the **division of a population into smaller groups**, called ‘strata’. Then the researcher randomly selects the final items proportionally from the different strata.

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

In addition, stratified sampling design leads to increased statistical efficiency. Each stratа (group) is highly homogeneous, but all the strata-s are heterogeneous (different) which reduces the internal dispersion. Thus, with the same size of the sample, greater accuracy can be obtained.

**Systematic Sampling**

This method is appropriate if we have a complete list of sampling** subjects arranged in some systematic order** such as geographical and alphabetical order.

The process of systematic sampling design generally includes first selecting a starting point in the population and then performing subsequent observations by using a constant interval between samples taken.

This interval, known as the sampling interval, is calculated by dividing the entire population size by the desired sample size.

For example, if you as a researcher want to create a systematic sample of 1000 workers at a corporation with a population of 10000, you would choose every 10th individual from the list of all workers.

**Cluster Random Sampling**

This is one of the popular types of sampling methods that randomly select members from a list which is too large.

A typical example is when a researcher wants to choose 1000 individuals from the entire population of the U.S. It is impossible to get a complete list of every individual. So, the researcher randomly selects areas (such as cities) and randomly selects from within those boundaries.

Cluster sampling design is used **when natural groups occur in a population**. The entire population is subdivided into clusters (groups) and random samples are then gathered from each group.

Cluster sampling is a very typical method for market research. It’s used when you can’t get information about the whole population, but you can get information about the clusters.

The cluster sampling requires heterogeneity in the clusters and homogeneity between them. Each cluster must be a small representation of the whole population.

**Non-probability Sampling**

The key difference between non-probability and probability sampling is that the first one does not include random selection. So, let’s see the definition.

**What is non-probability sampling?**

Non-probability sampling is a group of sampling techniques where the samples are collected in a way that does not give all the units in the population equal chances of being selected. Probability sampling does not involve random selection at all.

**For example**, one member of a population could have a 10% chance of being picked. Another member could have a 50% chance of being picked.

Most commonly, the units in a non-probability sample are selected on the basis of their accessibility. They can be also selected by the purposive personal judgment of you as a researcher.

**Advantages of non-probability sampling**:

- When a respondent refuses to participate, he may be replaced by another individual who wants to give information.
- Less expensive
- Very cost and time effective.
- Easy to use types of sampling methods.

**Disadvantages** **of non-probability sampling**:

- The researcher interviews individuals who are easily accessible and available. It means the possibility of gathering valuable data is reduced.
- Impossible to estimate how well the researcher representing the population.
- Excessive dependence on judgment.
- The researchers can’t calculate margins of error.
- Bias arises when selecting sample units.
- The correctness of data is less certain.
- It focuses on simplicity instead of effectiveness.

**Types of Non-Probability Sampling Methods**

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

**Convenience Sampling**

As the name suggests, this method involves collecting **units that are the easiest to access**: your local school, the mall, your nearest church and etc. It forms an accidental sample. It is generally known as an unsystematic and careless sampling method.

Respondents are those “who are very easily available for interview”. For example, people intercepted on the street, Facebook fans of a brand and etc.

This technique is known as one of the easiest, cheapest, and least time-consuming types of sampling methods.

**Quota Sampling**

Quota sampling methodology aims to create a sample where the groups (e.g. males vs. females workers) are **proportional to the population**.

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

For example, if your population has 40% female and 60% males, your sample should consist those percentages.

Quota sampling is typically done to ensure the presence of a specific segment of the population.

**Judgment Sampling**

Judgmental sampling is a sampling methodology where the researcher selects the units of the sample **based on their knowledge**. This type of sampling methods is also famous as purposive sampling or authoritative sampling.

In this method, units are selected for the sample on the basis of a professional judgment that the units have the required characteristics to be representatives of the population.

According to https://explorable.com/ “The process involves nothing but purposely handpicking individuals from the population based on the authority’s or the researcher’s knowledge and judgment.”

Judgmental sampling design is used mainly when a restricted number of people possess the characteristics of interest. It is a common method of gathering information from a very specific group of individuals.

**Snowball Sampling**

Snowball sampling isn’t one of the common types of sampling methods but still valuable in certain cases.

It is a methodology where researcher **recruits other individuals for the study**. This method is used only when the population is very hard-to-reach.

For example, these include populations such as working prostitutes, current heroin users, people with drug addicts, and etc. The key downside of a snowball sample is that it is not very representative of the population.

**Conclusion**

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

By knowing and understanding some basic information about the different types of sampling methods and designs, you can be aware of their advantages and disadvantages.

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

In the real research world, the official marketing and statistical agencies prefer probability-based samples. While it would always be good to perform a probability-based sampling, sometimes other factors have to be considered such as cost, time, and availability.

thank you.. helped me a lot..

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