Recent work on ASR for endangered languages has focused on fine-tuning a pretrained multilingual model to a small amount of data in the target language, but even with large and diverse pretrained models, error rates can be stubbornly high. Including additional data from the target language can yield improvements, but acquiring this kind of data for endangered languages is challenging. One commonly proposed alternative is to leverage data in a more highly resourced language from the same language family, but there has been remarkably little work evaluating the real-world utility of this practice. In this short paper, we explore the relevance of phylogenetic relatedness of transfer data for improving ASR for endangered languages with minimal training resources. We find, unexpectedly, that augmenting with unrelated donor language data improves word error rate (WER) more than related language data. In addition, the performance improvement observed via transfer of unrelated data is larger than that observed when nearly doubling the quantity of in-language training data. These surprising results hold even when controlling for factors such as acoustic similarity, audio quality, and subword overlap. Our findings point the way to practical new approaches for improving ASR for endangered languages.