Small sample size solutions : A Guide for Applied Researchers and Practitioners

Abstract

Researchers often have difficulties collecting enough data to test their hypotheses, either because target groups are small (e.g., patients with severe burn injuries); data are sparse (e.g., rare diseases), hard to access (e.g., infants of drug-dependent mothers), or data collection entails prohibitive costs (e.g., fMRI, measuring phonological difficulties of babies); or the study participants come from a population that is prone to drop-out (e.g., because they are homeless or institutionalized). Such obstacles may result in data sets that are too small for the complexity of the statistical model needed to answer the research question. Researchers could reduce the required sample size for the analysis by simplifying their statistical models. However, this may leave the “true” research questions unanswered. As such, limitations associated with small data sets can restrict the usefulness of the scientific conclusions and might even hamper scientific breakthroughs.