Final answer:
The variability of scores in a sample is generally less than the variability of scores in the population from which the sample was obtained because a sample does not capture the full extent of population variability.
Step-by-step explanation:
The variability of the scores in a sample tends to be less than the variability of the scores in the population from which the sample was obtained. In statistical terms, a sample is generally seen as a subset of the population, and due to its limited size compared to the whole population, the variability within a sample will not capture the full extent of the variability present in the entire population. This is because a sample may not include all the possible variations that exist within a population.
For example, when we calculate measures such as variance and standard deviation in a sample, we use a slightly different formula than when we calculate these measures for a population. The population variance or standard deviation is considered a parameter, while the sample variance or standard deviation is considered a statistic—a statistic that is c. slightly less likely than the population parameters to show the full variability.
This relationship is also seen in the concept of sampling variability, which refers to how much the sample statistics would vary from sample to sample, due to the fact that different samples may include different subsets of the population's values.