What type of sampling plan must be used if an inference is to be made about the whole population?

To draw valid inferences about an entire population, it is essential to use a statistical sampling plan at a stated confidence level. This type of sampling uses statistical methods to ensure that the sample accurately represents the larger population from which it is drawn. By specifying a confidence level, researchers can quantify the degree of certainty associated with their estimates, which is vital for making informed decisions based on the data collected.

A statistical sampling plan allows investigators to apply probability theory to generalize their findings beyond the sample. This approach helps to manage sampling errors and enables the calculation of confidence intervals and margins of error, which are crucial for understanding the reliability of the inferences made.

Other sampling methods, such as random, qualitative, or convenience sampling, may have specific uses but could lead to biased estimates or lack the rigorous statistical backing necessary for population-wide inferences. Random sampling, while beneficial, does not automatically guarantee that the sample will be representative unless a specified statistical framework is used. Similarly, qualitative and convenience sampling techniques may be useful for exploratory research but do not provide a valid basis for making broad generalizations about a population.