Different types of Probability Sampling

There are several types of probability sampling techniques:

• Simple Random sampling
• Systematic sampling
• Stratified sampling
• Cluster sampling

1. Simple Random Sampling

Every item has an equal opportunity of being picked without their being any impact on others following them. The selection is like picking a name out of a hat.

Simple random sampling is the most basic and straightforward method. In this method, each element in the population has an equal chance of being selected. This is typically done by assigning a unique identification number to each element and then using a random number generator to select the required number of elements.

Example: Imagine you are a quality control manager at a chocolate factory, and you want to test the quality of chocolate bars. You have a large production line, and you want to select a sample of chocolate bars for testing. You assign a unique serial number to each chocolate bar, and then you use a random number generator to select 20 chocolate bars from the entire production. This ensures that each chocolate bar has an equal chance of being tested.

2. Systematic Sampling

It involves selecting every nth item from a population. For example, if you have a list of students and you select every 10th student, that’s systematic sampling.

Systematic sampling involves selecting every “nth” element from a list or sequence. The starting point is chosen randomly, and then every nth element is included in the sample.

Example: If you are running a customer feedback program for a retail store with a daily footfall of 300 customers, you can select a systematic sample by choosing every 10th customer as they enter the store. This method is efficient, especially when customers arrive in a regular sequence.

3. Stratified Sampling

It divides the population into subgroups or strata based on certain characteristics (e.g., age, gender), and then samples are randomly selected from each stratum. This method ensures representation from each subgroup

In stratified sampling, the population is divided into subgroups or strata based on certain characteristics that are relevant to the research, such as age, gender, income, or location. A random sample is then taken from each stratum, and the samples from each stratum are combined to form the complete sample.

Example: Let’s say you are conducting a survey on smartphone preferences. You divide the population into strata based on age groups: under 18, 18-35, and over 35. Within each stratum, you randomly select a sample of individuals. This ensures that you get a representative sample of smartphone preferences across different age groups.

4. Cluster Sampling

It divides the population into clusters or groups, often based on geographical regions. A random sample of clusters is selected, and then all items within the chosen clusters are included in the sample.

Cluster sampling involves dividing the population into clusters, and then a random sample of clusters is selected. Instead of surveying every individual in the selected clusters, you survey all individuals within those clusters. This method is useful when it’s difficult or expensive to create a complete list of the entire population.

Example: Suppose you are conducting a survey on traffic congestion in a large city. Instead of trying to survey every street in the city, you randomly select a few neighborhoods (clusters) within the city. Then, you survey all streets and intersections within those selected neighborhoods. This method is useful when it’s impractical to survey the entire city due to time or resource constraints.

When conducting research and analyzing data in order to draw broad conclusions, the quality of inferences is crucial. Only the efficient statistical method known as probability sampling can achieve this. Probability sampling assures that every person or component of a population has an equal chance to be included in a sample that is randomly selected from the entire population. Such sample replications are free and unbiased since they come from an uncompromised representation of a large population, which increases their capacity to offer crucial information about the entire populace.