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nehap12
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Data analysts use various sampling techniques to select a subset of data from a larger population for analysis. Sampling is essential when it's impractical or costly to analyze the entire dataset. Different sampling methods serve different purposes and are chosen based on the research objectives and characteristics of the data. Here are some common sampling techniques used by data analysts:
- Simple Random Sampling:
- In simple random sampling, each member of the population has an equal chance of being selected. This is typically done using random number generators or drawing lots.
- Stratified Sampling:
- In stratified sampling, the population is divided into distinct subgroups or strata based on certain characteristics (e.g., age, gender, location). Samples are then randomly selected from each stratum. This ensures representation from all subgroups.
- Systematic Sampling:
- Systematic sampling involves selecting every nth element from the population after a random starting point is chosen. For example, if you want to sample every 10th person from a list, you would start with a random individual and then select every 10th person after that.
- Cluster Sampling:
- In cluster sampling, the population is divided into clusters, and a random selection of clusters is made. Then, all members within the selected clusters are included in the sample. This method is useful when it's challenging to sample individuals directly.
- Convenience Sampling:
- Convenience sampling involves selecting individuals or data points that are easy to access or readily available. While convenient, this method can introduce bias because it may not represent the entire population.