Having discussed the various probability and non probability sampling designs we now need to focus attention on the second aspect of the sampling design issue that is the sample size. Suppose we select 30 people from a population of 300 through a simple random sampling procedure. Will we be able to generalize our findings to the population with confidence since we have chosen a probability design that has the most generalizability? What is the sample size that would be required to make reasonably precise generalizations with confidence? What do precision and confidence mean? These issues will be considered now.
A reliable and valid sample should enable us to generalize the finds from the sample to the population under investigation. In other words the sample statistics should be reliable estimates and reflect the population parameters as closely as possible with a narrow margin of errors. No sample statistic is going to be exactly the same as the population parameters no matter how sophisticated the probability sampling design is. Remember that the very reason for a probability design is to increase the probability that the sample statistics will be as close as possible to the population parameters. Though the point estimate made within which mean will lie.
A reliable and valid sample should enable us to generalize the finds from the sample to the population under investigation. In other words the sample statistics should be reliable estimates and reflect the population parameters as closely as possible with a narrow margin of errors. No sample statistic is going to be exactly the same as the population parameters no matter how sophisticated the probability sampling design is. Remember that the very reason for a probability design is to increase the probability that the sample statistics will be as close as possible to the population parameters. Though the point estimate made within which mean will lie.
