Random Sampling vs Non-Probability Sampling
Below are the differences between Probability Sampling vs Non-Probability Sampling:
Aspect | Probability Sampling (Random Sampling) | Non-Probability Sampling |
---|---|---|
Method of Selection | Random or systematic selection based on a known probability distribution. | Non-random or subjective selection without a known probability distribution. |
Representativeness | Likely to yield a representative sample of the population, reducing bias. | May result in a non-representative sample, potentially introducing bias. |
Generalizability | Results are more easily generalized to the entire population. | Generalizability may be limited due to the potential for bias in the sample. |
Sample Size Determination | Easier to calculate sample size requirements based on probability theory. | Sample size determination may be more arbitrary and less statistically rigorous. |
Types of Probability Sampling Methods | Simple Random Sampling, Systematic Sampling, Stratified Sampling, Cluster Sampling. | Convenience Sampling, Judgmental Sampling, Snowball Sampling, Quota Sampling. |
Statistical Inference | Suitable for making statistical inferences and drawing conclusions about the population. | Limited suitability for making statistical inferences about the entire population. |
Precision and Accuracy | Generally provides more precise and accurate estimates when properly conducted. | May result in less precise and accurate estimates, depending on the sampling method used. |
Random Error | Random errors can be quantified and controlled, making it more reliable. | More susceptible to random errors due to non-random selection methods. |
Bias Control | Better control over selection bias and the potential for under representation or over representation. | May introduce selection bias and potential for under representation or over representation. |
Examples | Drawing a random sample of registered voters using a random number generator. | Surveying people who are conveniently available in a shopping mall for an opinion poll. |
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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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