The potential effectiveness of statistical haplotype inference makes it an area of active exploration over the last decade. There are several complications of statistical inference, including: the same algorithm can produce different solutions for the same data set, which reflects the internal algorithm variability; different algorithms can give different solutions for the same data set, reflecting the discordance among algorithms; and the algorithms per se are unable to evaluate the reliability of the solutions even if they are unique, this being a general limitation of all inference methods. With the aim of increasing the confidence of statistical inference results, consensus strategy appears to be an effective means to deal with these problems. Several authors have explored this with different emphases. Here we discuss two recent studies examining the internal algorithm variability and among-algorithm discordance, respectively, and evaluate the different outcomes of these analyses, in light of Orzack (2009) comment. Until other, better methods are developed, a combination of these two approaches should provide a practical way to increase the confidence of statistical haplotyping results.