What type of sampling requires that every member of the population has an equal chance of being selected?

What type of sampling requires that every member of the population has an equal chance of being selected?

• A. Convenience sampling
• B. Stratified sampling
• C. Simple random sampling
• D. Cluster sampling

Answer: (C)

Simple random sampling is a method where every member of the population has an equal chance of being selected. This technique ensures that the sample is representative of the entire population, reducing biases that can occur when certain members have a higher or lower chance of being chosen. By utilizing random selection, all individuals are treated equally, which strengthens the validity of the conclusions drawn from the sample data. This method serves as a fundamental approach in statistical studies, promoting fairness and randomness, essential for accurate sampling and inference in research. In contrast, other sampling methods, such as convenience sampling, focus on selecting individuals who are easily accessible, which can lead to significant biases. Stratified sampling involves dividing the population into subgroups and sampling from each of these, which is not equal for every member overall. Cluster sampling entails dividing the population into clusters, then randomly selecting entire clusters rather than individuals, which again does not guarantee equal selection chances for all members.

Simple random sampling is a method where every member of the population has an equal chance of being selected. This technique ensures that the sample is representative of the entire population, reducing biases that can occur when certain members have a higher or lower chance of being chosen. By utilizing random selection, all individuals are treated equally, which strengthens the validity of the conclusions drawn from the sample data. This method serves as a fundamental approach in statistical studies, promoting fairness and randomness, essential for accurate sampling and inference in research.

In contrast, other sampling methods, such as convenience sampling, focus on selecting individuals who are easily accessible, which can lead to significant biases. Stratified sampling involves dividing the population into subgroups and sampling from each of these, which is not equal for every member overall. Cluster sampling entails dividing the population into clusters, then randomly selecting entire clusters rather than individuals, which again does not guarantee equal selection chances for all members.