The sample size of a clinical study influences how precisely it is able to estimate parameters of interest (e.g. an odds ratio reflecting the prognostic value of a biomarker, the probability of severe treatment toxicity, a hazard ratio reflecting the effectiveness of a new intervention compared to a control, etc.). Clinical studies are generally designed to gather enough information to provide estimates of these parameters with a level of precision that is appropriate for the research context. For example, late phase III trials require high precision to produce robust evidence to confidently address questions of direct relevance to standard clinical practice (e.g. does the evidence support the routine use of a new treatment in place of an established treatment). In contrast, early phase clinical trials require only enough precision to adequately answer questions relating to intervention development (e.g. is the preliminary evidence of the activity and safety of an intervention sufficiently persuasive to warrant its further evaluation?). The size of a clinical study will be unavoidably constrained in some circumstances however (e.g. rare diseases, unique populations, highly tailored therapies), and estimates from these necessarily small studies may be at risk of being so imprecise that it becomes especially difficult to distinguish true effects from the play of chance. In small study settings, study design and analysis methods must be carefully selected and meticulously implemented to maximise the value of the study. Design and analysis considerations will particular applicability to small clinical studies, as well as guidance on the interpretation of results obtained from small studies, will be presented.