The analysis of functional magnetic resonance imaging (fMRI) data involves multiple stages of data pre-processing before the activation can be statistically detected. Spatial smoothing is a very common pre-processing step in the analysis of functional brain imaging data. This study presents a broad perspective on the influence of spatial smoothing on fMRI group activation results. The data obtained from 20 volunteers during a visual oddball task were used for this study. Spatial smoothing using an isotropic gaussian filter kernel with full width at half maximum (FWHM) sizes 2 to 30 mm with a step of 2 mm was applied in two levels - smoothing of fMRI data and/or smoothing of single-subject contrast files prior to general linear model random-effects group analysis generating statistical parametric maps. Five regions of interest were defined, and several parameters (coordinates of nearest local maxima, t value, corrected threshold, effect size, residual values, etc.) were evaluated to examine the effects of spatial smoothing. The optimal filter size for group analysis is discussed according to various criteria. For our experiment, the optimal FWHM is about 8 mm. We can conclude that for robust experiments and an adequate number of subjects in the study, the optimal FWHM for single-subject inference is similar to that for group inference (about 8 mm, according to spatial resolution). For less robust experiments and fewer subjects in the study, a higher FWHM would be optimal for group inference than for single-subject inferences.