Spatial interpolation methods are frequently used to characterize patterns in soil properties over various spatial scales provided that the data are abundant and spatially dependent. Establishing these criteria involved comparisons of abundant data from many fine‐scaled (<100 ha) investigations. In this study we investigated whether it was appropriate to use spatial interpolation methods with limited (n = 46), coarse‐scaled (1188 ha) soils data from a Vertisol plain. Methods investigated included ordinary kriging, inverse‐distance weighting, and thin‐plate smoothing splines with tensions. Comparison was based on accuracy and effectiveness measures, and analyzed using ANOVA and pairwise comparison t‐tests. Results indicated that spatial interpolation was appropriate when the data exhibited smooth and consistent patterns of spatial dependency within the study area and the selected ranges of estimation and weighting used in this investigation. Nine of twelve soil properties we investigated exhibited characteristics other than these, however, including independent data, variable and erratic behavior, and extreme values. Our sample design may have been an important factor as well. Ordinary kriging and inverse‐distance weighting were similarly accurate and effective methods; thin‐plate smoothing splines with tensions was not. Results illustrate that sample size is as important for coarse‐scale investigations as it is for fine‐scale investigations with most soils data. However, our ability to predict successfully with some of our data raises the question as to the exact nature of the relationship between accuracy, sample size, and sample spacing, and to what extent these factors are related to the property under investigation, particularly when data are limited.