About sampling methods in statistics

This is an article about sampling methods in statistics.

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Today we will learn about sampling methods in statistics.

In statistics, sampling refers to the process of extracting a subset of samples from a population.

This allows you to obtain information about the entire population and infer the characteristics of the population.

Below is a description of the sampling methods commonly used in statistics.

Simple Random Sampling

This is a method of sampling in such a way that each object has an equal probability of being selected.

This ensures that each individual is selected independently and that the selected sample is unbiased.

Systematic Sampling

This is a method of sampling at regular intervals, in which the first sample is selected at random, and then subsequent samples are selected at regular intervals.

Stratified Sampling

This is a method of dividing the population into several strata and then randomly drawing samples from each stratum.

Samples from each layer are selected to represent the characteristics of that layer.

Cluster Sampling

This is a method of dividing the population into several clusters, randomly selecting some clusters, and examining all individuals within those clusters.

Resampling of measurements

A method of drawing new samples based on samples that have already been drawn, using methods such as Bootstrap and Jackknife.

Conclusion

It is important that the sampling method selects a sample to represent the characteristics of the population.

Each method requires sampling by selecting an appropriate method depending on the specific situation or research purpose.

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