In a stratified random sampling method,
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A. B. C. D.Explanation
The population is divided into strata or subcategories and a sample is randomly selected from each strata.
In a stratified random sampling method, option B is the correct answer. Let's break down the explanation to understand why.
Stratified random sampling is a sampling technique that involves dividing the population into distinct subgroups or strata based on certain characteristics. The goal is to ensure that each stratum is represented in the sample in proportion to its size in the population. This method allows for greater precision and accuracy in estimating population parameters compared to simple random sampling.
Now, let's examine the other options to understand why they are not correct:
Option A states that a sample is selected by randomly generating an integer, N, and selecting every Nth member of the population. This description aligns with systematic random sampling, where a starting point is randomly selected, and then every Nth member is chosen. However, this is not the definition of stratified random sampling.
Option C suggests that a sample is selected by drawing numbers from a normal probability distribution. This statement describes a sampling method called normal random sampling, which is not the same as stratified random sampling. Normal random sampling involves selecting sample elements based on their probability distribution, often assuming a normal distribution.
Option D claims that a sample is selected such that every member of the population has the same chance of being selected. This description fits the concept of simple random sampling, where each member of the population has an equal probability of being selected. However, it does not capture the essence of stratified random sampling, which involves dividing the population into groups or strata and selecting members from each group.
To summarize, stratified random sampling (option B) involves dividing the population into subgroups, selecting members from each subgroup, and ensuring that the sample represents the proportions of each subgroup found in the population. This technique helps improve the accuracy and precision of the sample, particularly when the subgroups differ significantly from one another.