Understanding Probability Sampling Principles

Probability sampling refers to a set of techniques used in research where every member of the population has a known, non-zero chance of being selected for the sample. This randomness is crucial for ensuring that the sample is representative of the larger population and helps minimize bias.

Evaluating Statements on Probability Sampling

Let's examine each statement to determine its validity concerning probability sampling:

• Statement A: Every item of the universe has equal chance of inclusion
  This is a fundamental principle, especially in simple random sampling, a core probability sampling method. While some probability methods might assign known, unequal probabilities (e.g., stratified sampling), the core concept ensures each unit has a chance, often equal, which increases the likelihood of a representative sample. Hence, this statement is generally considered true.
• Statement B: There is always the danger of bias
  Probability sampling methods are specifically designed to mitigate or eliminate sampling bias by introducing randomness. Bias is a significant concern primarily in non-probability sampling methods where selection isn't random. Therefore, this statement is false in the context of probability sampling.
• Statement C: Sample has same composition and characteristic as that of universe
  Probability sampling aims for representativeness, meaning the sample's characteristics should closely mirror those of the population. While perfect replication isn't guaranteed due to random variation, probability methods provide the best framework for achieving this goal compared to non-probability methods. It is a desired outcome and a key advantage. Thus, this statement is considered true in principle.
• Statement D: Sample selection is based on researcher's convenience
  This statement describes non-probability sampling techniques like convenience sampling. Probability sampling relies strictly on random selection processes, not on the researcher's convenience or judgment, to ensure objectivity. Hence, this statement is false.
• Statement E: Individual units can be selected by lottery method
  The lottery method is a classic example of simple random sampling, a type of probability sampling. It ensures that every unit has an equal and independent chance of being selected. Therefore, this statement is true.

Key Characteristics of Probability Sampling

Based on the analysis, the statements that hold true for probability sampling are A, C, and E:

• A: Equal chance of inclusion is a key aspect.
• C: The goal of having a sample representative of the universe's characteristics is central.
• E: Methods like the lottery system are practical implementations of probability sampling.

Statements B and D are characteristic of non-probability sampling.

Final Conclusion

The combination of statements A, C, and E accurately reflects the principles and aims of probability sampling in research design.