Random Sampling
What is Random Sampling Method?
Random sampling is a method used in research to select a sample from a larger population in an unbiased way.
What is Simple Random Sampling?
Simple random sampling is a statistical method used to select a sample from a larger population in such a way that every possible subset of the population has an equal chance of being selected.
Why Random Sampling is Important?
Random Sampling is vital because it helps ensure the sample represents the population, making the research findings more reliable and generalisable.
What Are Some Common Methods for Random Sampling?
Common random sampling methods include:
- Simple Random Sampling
- Stratified Random Sampling
- Systematic Sampling
- Cluster Sampling
How Does Simple Random Sampling Work?
Simple Random Sampling works by randomly selecting individuals or items from a population so that each has an equal chance of being chosen, ensuring a representative sample.
What Is Stratified Random Sampling?
Stratified Random Sampling divides the population into distinct subgroups (strata) based on certain characteristics, then randomly samples from each stratum to ensure representation of various groups within the population.
When Is Systematic Sampling Used?
Systematic Sampling is used when there’s a need for simplicity and efficiency. It involves selecting every kth item from a list, starting with a random item. It’s useful for large populations and can provide a representative sample.
What Is Cluster Sampling?
Cluster Sampling involves dividing the population into clusters, randomly selecting some clusters, and then sampling all individuals within the chosen clusters. It’s useful for geographically dispersed or large populations.
Are There Any Drawbacks to Random Sampling?
Yes, there are drawbacks to random sampling, including the potential for bias due to chance, non-response, and under representation of certain groups. Large populations can be costly and time-consuming to sample completely.
How Do You Calculate Sample Size for Random Sampling?
To calculate the sample size for random sampling, consider factors like population size, desired confidence level, margin of error, and population variability. Formulas such as the one for simple random sampling help determine the required sample size.
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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