 # Types of Sampling Method: Learn 15 Sampling Methods for Data Scientist

### GET FREE AMZAON AUDIOBOOKS      The statistic is the science that deals with developing and studying method for collecting, analyzing and interpreting the data.  In another way you can say in statistics, we have data and want to know something from the data. While you go through the statistics you will use the term Population and Sample Again and again. Therefore you must clearly understand these terms before knowing all  the types of sampling method.

### Population ( Country )

In this, you always consider every member of the group you want to study.

### Sample ( State )

The sample is the subset of the Population. It means you take a random member from the population.

### Parameter and Statistic

A parameter is the characteristics of the population and you will use it in statistical analysis. Whereas statistics is the characteristic of the sample and we use it to define the statistical inferences to sample in describing the population.

You should also know the term Variable. It is a characteristic that describes the member of the sample.  For example Age, Salary, Gender, and Place. In addition, It can be discreet and continuous. As for example, Gender and Place are discrete and Age and Salary are continuous.

## Sampling

Sampling is a technique to reflect the results of the entire population by studying the results of each sample taken from the population.

There are two types of sampling methods

1. Probability Sampling Method
2. Non Probability Sampling Method

### Probability Sampling Method

In probability sampling, we take members of the population that have equal or non zero probability. It means each member have equal chances of selection for reflecting the population.

Types of Probability Sampling Method

1. Simple Sampling
2. Systematic Sampling
3. Stratified Sampling
4. Cluster Sampling

### Simple Sampling

In the simple sampling, all the members of the population have equal chances of selection. It means you can randomly select any member for sampling.

### Systematic Sampling

From the name you can think what is the Systematic Sampling. In this sampling, you pick up the members from the population through a well-defined system to make a Sample. For example, you have to select the top 3 students from each class. In this You have a given condition and you have to pick up sample according to that condition.

### Stratified Sampling

Stratified sampling is more convenient than Simple Sampling. In this, you first Stratified to make an ordered or categorized samples from the population called as Strata. In fact, It is a well defined and organized network. Now you can choose members from each stratum for making a sample.

### Cluster Sampling

It is a very important sampling technique. In this sampling, you divide the population into groups call as clusters. Now you make a simple random sample (stage 1)  by selecting a member from each cluster or group.

Now if you make another cluster from the simple random subsample and then selecting a member from each cluster fo then it will be stage 2. It is called Multi-stage sampling and this is only done to reduce the cost of sampling.This sampling method is generally used in marketing purpose.

### Non Probability Sampling Method

In the non-probability sampling, each member of the population does not have an equal chance of selection. It is also called Non-representative sampling.

### Types of Non-Probability Sample Method

1. Quota Sampling
2. Convenience Sampling
3. Judgment Sampling
4. Purposive Sampling

### Quota Sampling

In the quota sampling, you categorized the data into some weightage. You choose members from the population to make sample keeping in mind the weightage.

Example:

Let’s say you have 100 people. There 2% of people are Upper Class, 10% on Medium Class and 30% in the lower class. Then in Quota Sampling, you will select 2% members from Upper Class and 10% on Medium class and 30% from lower class from the population that is from 100 people.

### Convenience Sampling

In convenience sampling, you select members from the population according to your convenience.

Example

In a box there 100 colored balls, 50 is red, 10 is green and 5 is Yellow colored balls. Let’s say for my convenience I will not choose Yellow colored balls for sampling. I will choose only from 50 red and 10 green balls. It is called Convenience Sampling, choosing samples as my convenience.

### Purposive Sampling

In a Purposive Sampling, you select members for sampling on the basis of the objective of the study. It is very useful when you want to reach a particular or targeted sample quickly. It is also known as Judgemental Sampling

#### Types of Purposive Sampling

Purposive Sampling has the following major types.

1. Maximum Variation/Heterogeneous Purposive Sample
2. Homogeneous Purposive Sample
3. Typical Case Sampling –
4. Extreme/Deviant Case Sampling
5. Critical case Sampling
6. Total Population Sampling
7. Expert Sampling

#### Maximum Variation/Heterogeneous Purposive Sample

From the name you can somewhat understand what it is. In this sampling technique, we select results from the more varied or Heterogeneous cases for a particular event or phenomena. Its main task to find the insights of an event or issue.

Example:

A researcher wants to know the insights of an issue in the society. He/She will ask a different kind of persons in the society for finding the views on that issue through polls.

#### Homogeneous Purposive Sample

In this sampling, you select on the basis of the set of characteristics from the population.

Example:

You have made a supplement for the fitness. In addition, you want to know how this protein powder is useful on bodybuilding and its quality. So you will only ask that person that is using that protein powder. Here characteristic is bodybuilders that are using the protein powder.

#### Typical Case Sampling

It is called as case sampling. You select sample on the cases. It means you relate the sampling results to the selected cases and find the relation between them.

Example:

You want to study the effects of changed curriculum on the average students then you will make a sample from the average students (case) to find the relationship.

#### Extreme/Deviant Case Sampling

This sampling is used when the researcher wants to study the effects of the outliers on the results previously obtained. How the results diverge from the normal results.

#### Critical case Sampling

Here you make sampling on a specific case and the researcher made an exception that this result will be same as the like cases.

#### Total Population Sampling

In this researcher study the set of characteristics of the entire population. It is mostly used to generate reviews of an event and phenomena and to identify the same characteristics groups in the entire population.

#### Expert Sampling

From the name you can understand what it is? You are an expert on a field or expertise and then start sampling on the basis of your knowledge.

## Conclusion

All these are sampling methods. As a researcher, you should know these sampling techniques before trying to accumulate sample data. Data Scientist does a vast analysis of the data and therefore these methods help them to know insights of the data sample and its effect.

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