Random sample
Random sampling is a technique used in statistics where a subset of individuals are chosen from a larger set. The selection is made in such a way that each individual has an equal probability of being chosen. This method is used when the population is too large to collect data from everyone.
Overview[edit | edit source]
Random sampling is a fundamental concept in the field of statistics. It is a technique used to select individuals from a population in a way that is unbiased. The main advantage of random sampling is that it provides a true representation of the population, which makes the results of the study more valid and reliable.
Types of Random Sampling[edit | edit source]
There are several types of random sampling, including:
- Simple Random Sampling: This is the most basic form of random sampling. Each individual in the population has an equal chance of being selected. This method is most effective when the population is relatively homogeneous.
- Stratified Random Sampling: In this method, the population is divided into subgroups, or strata, based on certain characteristics. Then, individuals are randomly selected from each stratum.
- Cluster Random Sampling: In this method, the population is divided into clusters, and a random sample of clusters is selected. All individuals within the selected clusters are included in the sample.
- Systematic Random Sampling: In this method, a random starting point is chosen, and then every nth individual is selected.
Advantages and Disadvantages[edit | edit source]
Random sampling has several advantages and disadvantages:
Advantages
- It provides a true representation of the population.
- It eliminates bias in the selection process.
- The results can be generalized to the larger population.
Disadvantages
- It can be difficult to achieve in practice.
- It may not be the most efficient method if the population is large.
- It may not be possible if the population is not well-defined.
See Also[edit | edit source]
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