Simple baseline studies correlate average perfusion levels measured at rest with individual variables, or contrast subject groups as in case-control studies. In this methodological work, we summarize some formal properties of the design of these studies, and investigate the sources of variance that characterize data acquired with the arterial spin labeling technique, with the purpose of alerting users to the main sources of variation that determine background variance and affect the power of statistical tests. This design typology is characterized by two variance components: between acquisitions and between subjects. We show that variation between acquisitions is affected by the presence of large vessels and venous sinuses, with potential adverse effects especially in the temporal and insular regions, and provide maps of the number of acquisitions or subjects required to reach the desired estimate precision. Furthermore, we show that the largest source of variation between subjects is captured by global perfusion levels, and can in principle be removed by adjusting the data. Significance levels, however, are not always only improved by the adjustment procedure; we provide an example in the correlation with age, and attempt to explain the consequences of the adjustment with the help of a principal component analysis of the data. We also show the existence of variation between subjects in the perfusion in the territory of the posterior cerebral artery and in hemispheric asymmetry.