Biodistance analysis can elucidate various aspects of past population structure. The most commonly adopted measure of divergence when estimating biodistances is the mean measure of divergence (MMD). The MMD is an unbiased estimator of population divergence but this property is lost when the dataset includes variables with very high or low frequency. In the present paper, we examine new measures of divergence based on untransformed binary data and the logit and probit transformations. It is shown that a measure of divergence based on untransformed data is a better unbiased estimator of population divergence. The conventional MMD is a satisfactory distance measure for binary data; however, it may produce biased estimations of population divergence when there are many traits with frequencies lower than 0.1 or/and greater than 0.9. Finally, the measures of divergence based on the probit and logit transformations are usually biased estimators.

生物距离分析可以阐明过去人口结构的各个方面。估算生物距离时最常用的差异度量是平均差异（MMD）。MMD是人口差异的无偏估计器，但是当数据集包含频率非常高或非常低的变量时，此属性将丢失。在本文中，我们研究了基于未转换的二进制数据以及logit和probit转换的新发散度量。结果表明，基于未转换数据的差异度量是更好的人口差异无偏估计。传统的MMD是令人满意的二进制数据距离度量；但是，当许多性状的频率低于0.1或/和大于0.9时，它可能会导致人口差异的偏向估计。最后，