How does probability sampling differ from non-probability sampling?

Probability sampling is characterized by the fact that every member of the population has a known, non-zero chance of being selected for the sample. This method employs random selection, which helps ensure that the sample is representative of the broader population, reducing biases that can affect the results. Examples of probability sampling techniques include simple random sampling, stratified sampling, and cluster sampling.

On the other hand, non-probability sampling methods, such as convenience sampling or purposive sampling, do not guarantee that every individual has a chance of being selected. This can lead to certain biases, as these methods often rely on subjective judgment or availability, which may not accurately reflect the overall population.

Understanding this distinction is crucial for researchers because the choice between these methods can significantly impact the validity and generalizability of their findings. While probability sampling may be more complex to implement, it offers stronger reliability and representativeness.