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Jan 28
1. Simple Random Sampling: This method involves selecting a sample from a population in such a way that each individual has an equal chance of being chosen. It can be done by assigning a unique number to each individual in the population and then using a random number generator to select the desired sample size.
2. Stratified Random Sampling: In this method, the population is divided into subgroups or strata based on certain characteristics. A random sample is then selected from each stratum in proportion to its size or importance. This ensures that each subgroup is represented in the sample, which can be useful when there are significant differences between the subgroups.
3. Cluster Sampling: Cluster sampling involves dividing the population into clusters or groups and then randomly selecting a few clusters to include in the sample. This method is often used when it is difficult or impractical to sample individuals directly, such as in large geographical areas or when the population is widely dispersed.
4. Systematic Sampling: Systematic sampling involves selecting every nth individual from a population after randomly selecting a starting point. For example, if the population size is 1000 and the desired sample size is 100, every 10th individual can be selected by randomly choosing a number between 1 and 10 as the starting point.
5. Convenience Sampling: Convenience sampling is a non-probability sampling method where individuals are selected based on their availability and willingness to participate. This method is often used in situations where it is difficult to access the entire population or when time and resources are limited. However, it may introduce bias as it does not ensure a representative sample.
2. Stratified Random Sampling: In this method, the population is divided into subgroups or strata based on certain characteristics. A random sample is then selected from each stratum in proportion to its size or importance. This ensures that each subgroup is represented in the sample, which can be useful when there are significant differences between the subgroups.
3. Cluster Sampling: Cluster sampling involves dividing the population into clusters or groups and then randomly selecting a few clusters to include in the sample. This method is often used when it is difficult or impractical to sample individuals directly, such as in large geographical areas or when the population is widely dispersed.
4. Systematic Sampling: Systematic sampling involves selecting every nth individual from a population after randomly selecting a starting point. For example, if the population size is 1000 and the desired sample size is 100, every 10th individual can be selected by randomly choosing a number between 1 and 10 as the starting point.
5. Convenience Sampling: Convenience sampling is a non-probability sampling method where individuals are selected based on their availability and willingness to participate. This method is often used in situations where it is difficult to access the entire population or when time and resources are limited. However, it may introduce bias as it does not ensure a representative sample.
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