When are inferential statistics typically used?

Inferential statistics are utilized when researchers want to make generalizations or predictions about a population based on a sample of data. This is especially relevant when there are varied sources for the data because using diverse data sets allows statisticians to draw broader conclusions and assess relationships between variables. The goal of inferential statistics is to make inferences that extend beyond the data collected, providing insights that can apply to larger groups.

In the context of varied sources for data, inferential statistics can incorporate different sets of data, leading to more robust conclusions and a more comprehensive understanding of the phenomena being examined. This approach is relevant, particularly when researchers want to establish the significance of their findings in a broader context or determine correlations that may exist across different datasets.

While large sample sizes can enhance the power of inferential statistics, the mere presence of a large sample does not inherently necessitate their use. Similarly, case study research and experimental research can both utilize different types of statistics depending on the nature of the study and the variables involved, but they are not exclusive to inferential statistics. Thus, utilizing varied sources aligns closely with the essential function of inferential statistics in extending insights accurately to a greater population based on sample analysis.