Types of Random Sampling
Random sampling relies on a method that involves a degree of random selection. It allows all eligible individuals an equal opportunity to be part of the sample drawn from the entire sample space. While it can be laborious and costly, probability sampling is a powerful tool for creating a representative sample of the population. There are four main categories of this sampling technique, which include:
Let’s discuss the these types in detail.
Simple Random Sampling
Simple random sampling involves randomly selecting items without any specific pattern or criteria. For example simple random sampling involves the unbiased, purely random selection of individuals from the population, where each member has an equal chance of being included. Each member of the population has an equal chance of being chosen, like drawing names from a hat.
Systematic Random Sampling
Selects individuals at regular intervals, offering an organized yet random way to choose a sample. For example in systematic random sampling, you select a starting point at random and then choose every ‘k’-th element from the population. It’s like selecting every n person from a list.
Stratified Random Sampling
Divides the population into distinct strata or subgroups and then randomly samples from each stratum, enhancing representation. For example, stratified random sampling involves dividing the population into subgroups or strata based on certain characteristics. Samples are then randomly chosen from each stratum in proportion to their size.
Cluster Random Sampling
Organises the population into clusters, randomly selects some of these clusters, and samples all individuals within the chosen clusters. For example, clustered sampling divides the population into clusters or groups, and then a random sample of clusters is chosen. All individuals within the selected clusters are included in the sample.
By utilising these techniques, probability sampling aims to provide reliable insights into the broader population while maintaining the essence of randomness.
Random Sampling Method
Random Sampling is a method of probability sampling where a researcher randomly chooses a subset of individuals from a larger population. In this method, every individual has the same probability of being selected. The researcher aims to collect data from as large a portion as possible of this randomly chosen subset.
In the field of statistics, sampling serves as the technique for selecting a portion of the population to draw statistical inferences. This subset’s characteristics allow us to estimate the attributes of the entire population. In the realm of market research, sampling methods fall into two primary categories: Random or probability sampling and non-probability sampling.
This article discusses the specific category of probability sampling known as random sampling and its types, formulas, advantages, examples, etc.
Table of Content
- What is Random Sampling?
- Random Sampling Definition
- Types of Random Sampling
- Simple Random Sampling
- Systematic Random Sampling
- Stratified Random Sampling
- Cluster Random Sampling
- How to Perform Simple Random Sampling
- When to Use Random Sampling
- Random Sampling Formula
- Advantages of Simple Random Sampling
- Disadvantage of Simple Random Sampling
- Random Sampling vs Non-Probability Sampling
- Random Sampling Examples
- Practice Questions on Random Sampling
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