Final answer:
Large sample experiments are more generalizable to the population, while single case studies provide in-depth analysis but limited generalizability. The choice of method should align with the research goals.
Step-by-step explanation:
The difference between large sample experiments and single case studies is that large sample experiments are more generalizable, meaning the results can be applied to a larger population. This is because a larger sample is better able to reflect the actual diversity of the population. However, they do not provide the same depth of information on each person as single case studies can. Single case studies offer a more in-depth analysis of an individual or a very small group, but they are less generalizable to the larger population because they focus on specific, and often rare, cases.
A random sample is effective because it ensures that everyone has the same chance of being part of the study, which helps to avoid bias and improves the representativeness of the sample. The major limitation of case studies is their inability to generalize findings to a larger population. In contrast, large sample experiments, with appropriate experimental controls, allow comparison between groups with the intent to isolate the impact of an independent variable, bolstering internal validity.
Therefore, while large sample experiments and single case studies have their respective advantages and disadvantages, both are important in understanding behavior and phenomena. Choosing the appropriate method depends on the research goals, whether it's the broad application of findings or a deep exploration of an individual case.