Random Sampling Formula
Formula of random sampling is mentioned as below:
P = 1 –[(N-1)/N] × [(N-2)/(N-1)] × . . . × [(N-n)/(N-(n-1))]
Where,
- P represents probability,
- n represents sample size, and
- N represents population.
In above formula cancelling 1-(N-n/n), it will yield a value of P = n/N.
So, sample getting selected for a chance of more than once
P = 1 – (1 – (1/N))n
Where,
- P represents probability,
- n represents sample size, and
- N represents population.
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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