Sure, here are three types of sampling explained:

1. Random Sampling: This is the purest form of sampling that provides equal chances of being selected to each member of a population. It is free from bias and can be easily analyzed statistically. The process involves defining the population, listing all the members, and then using a random method to select the sample.

2. Stratified Sampling: In this type of sampling, the population is divided into smaller groups known as strata, based on certain common characteristics like age, gender, income, etc. Then, a random sample is drawn from each stratum. This method ensures that the sample represents all the strata of the population.

3. Cluster Sampling: This method is used when the population is large and spread over a wide geographical area. In this method, the population is divided into clusters or groups. Then, a few clusters are randomly selected and all members of these selected clusters form the sample. This method is cost-effective and time-saving but it may have a higher sampling error compared to other methods.

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Sure, here are three types of sampling explained:

1. Random Sampling: This is the purest form of sampling that provides equal chances of being selected to each member of a population. It is free from bias and can be easily analyzed statistically. The process involves defining the population, listing all the members, and then using a random method to select the sample.

2. Stratified Sampling: In this type of sampling, the population is divided into smaller groups known as strata, based on certain common characteristics like age, gender, income, etc. Then, a random sample is drawn from each stratum. This method ensures that the sample represents all the strata of the population.

3. Cluster Sampling: This method is used when the population is large and spread over a wide geographical area. In this method, the population is divided into clusters or groups. Then, a few clusters are randomly selected and all members of these selected clusters form the sample. This method is cost-effective and time-saving but it may have a higher sampling error compared to other methods.

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Sure, here are three types of sampling explained:

1. Random Sampling: This is the purest form of sampling that provides equal chances of being selected to each member of a population. It is free from bias and can be easily analyzed statistically. The process involves defining the population, listing all the members, and then using a random method to select the sample.

2. Stratified Sampling: In this type of sampling, the population is divided into smaller groups known as strata, based on certain common characteristics like age, gender, income, etc. Then, a random sample is drawn from each stratum. This method ensures that the sample represents all the strata of the population.

3. Cluster Sampling: This method is used when the population is large and spread over a wide geographical area. In this method, the population is divided into clusters or groups. Then, a few clusters are randomly selected and all members of these selected clusters form the sample. This method is cost-effective and time-saving but it may have a higher sampling error compared to other methods.

explain three types of sampling

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