What is the method called when subjects from a known population are drawn randomly and each has an equal chance of selection?

What is the method called when subjects from a known population are drawn randomly and each has an equal chance of selection?

• A. Simple Random Sample
• B. Stratified Random Sample
• C. Cluster Random Sample
• D. Accidental Sampling

Answer: (A)

Simple random sampling means selecting from a known population in a way that gives every individual an equal, independent chance of being included. This usually requires a complete list of all members (a sampling frame) and a random mechanism—like a random number generator or drawing names—to pick the sample. Because everyone has the same probability, the method minimizes selection bias and helps ensure the sample reflects the population, making estimates of population parameters more trustworthy as the sample size grows. Other methods change how probability is allocated: stratified random sampling still uses random selection but within subgroups to ensure representation of key segments; cluster sampling selects whole groups and then samples within those groups, which can be more efficient but may increase sampling error if clusters differ; accidental or convenience sampling relies on who is readily available and does not provide equal chances of selection, leading to biased results.

Simple random sampling means selecting from a known population in a way that gives every individual an equal, independent chance of being included. This usually requires a complete list of all members (a sampling frame) and a random mechanism—like a random number generator or drawing names—to pick the sample. Because everyone has the same probability, the method minimizes selection bias and helps ensure the sample reflects the population, making estimates of population parameters more trustworthy as the sample size grows.

Other methods change how probability is allocated: stratified random sampling still uses random selection but within subgroups to ensure representation of key segments; cluster sampling selects whole groups and then samples within those groups, which can be more efficient but may increase sampling error if clusters differ; accidental or convenience sampling relies on who is readily available and does not provide equal chances of selection, leading to biased results.