Which method is commonly used in assessing random sample selection?

Using a random number generator for assessing random sample selection is a widely accepted method because it ensures that each item in a population has an equal chance of being selected, thereby minimizing bias. This approach is particularly useful in situations where a researcher needs a representative sample from a large dataset or population.

A random number generator can be easily implemented using software tools or programming libraries, making it a practical choice for both small and large sample sizes. The randomized nature of this method helps in achieving statistical validity in research and quality control processes by ensuring that every member is accounted for without any systematic selection patterns.

In contrast, systematic sampling can introduce bias if there is an underlying order in the population, while whole sampling involves assessing an entire population, which might not always be feasible or efficient. Point sampling is often used for specific applications like assessing particular points within a data set, but it does not provide the same level of randomness as a random number generator. Thus, the random number generator stands out as the most effective method for creating a truly random sample.