Applied dairy research is characterized by experiments for which financial and physical constraints permit only a small number of experimental units. With few units it is difficult to replicate treatments, and without replication experimental error cannot be estimated. The statistical analysis and interpretation of such experiments is problematic. However, if there have been several such experiments it may be possible to perform a combined analysis. Nine unreplicated experiments comparing effects of diet on the composition of cows' milk and on cheese characteristics were jointly analysed as an incomplete block design. This analysis method was contrasted with analyses of individual experiments. For cheese moisture, the key outcome measurement, the assessment of statistical significance concurred for the two methods in 13 out of 21 comparisons of treatments with the control. Sources of error variation allowed for under the two methods were delineated. The combined analysis paradigm provided stronger inference and a wider interpretation of results than could be achieved using analyses for individual experiments. Unequal replication of treatments and unequal concurrence of treatments within experiments over the series gave rise to a wide range of SED. The challenge of presenting results with unequal SED was addressed graphically using error bars. Attention to series design, in particular the apportioning of replication and treatment concurrence across the series of experiments, was shown to ameliorate presentation difficulties and, more importantly, to yield higher precision at no extra cost.