Summary of Part A

Part A established two linked principles. First, small-sample research is common and methodologically legitimate when the design, question, and method are matched to the information available. Second, good small-sample work begins before analysis, with focused objectives, realistic research questions, appropriate outcomes, and claims scaled to what the data can support.

Chapter 1 explained why asymptotic approximations, default large-sample habits, and apologetic framing can all mislead when datasets are modest. Chapter 2 translated that logic into practical design choices by distinguishing exploratory from confirmatory aims, showing how to move from objective to hypothesis, comparing outcome scales, and introducing effect sizes, minimum detectable effects, and pilot-study purposes as planning tools.

The practical lesson is straightforward. Small-sample work is strongest when the question, outcome, design, and analysis are aligned before the data are interpreted. That foundation leads directly to the next part, which turns to the core analytic methods used to answer those questions.