Final answer:
Nonprobability samples, like convenience samples, are characterized by not giving every individual an equal chance of selection, resulting in potential bias and limited generalizability. Random sampling methods are used to create a more representative sample of the population.
Step-by-step explanation:
The defining characteristic of nonprobability samples is that not every member of the population has an equal chance of being included in the sample. Convenience sampling is a common nonprobability sampling method where individuals are chosen based on ease of access rather than random selection. This can lead to biased results because the sample may not represent the overall population accurately.
In contrast, random sampling methodologies like simple random sampling, stratified sampling, cluster sampling, and systematic sampling aim to reduce bias by giving every individual in the population an equal opportunity to be selected. This ensures that the sample is more likely to be representative of the population, making it possible to generalize the findings to a larger group.
While convenience sampling is easy and low-cost, it limits the ability to extrapolate findings beyond the sample since it may not accurately capture the diversity of the entire population. Therefore, when results need to be generalized, random sampling is preferable despite being more complex and potentially costlier to implement.