In evolutionary biology research, we often approach questions on males and females differently. Sometimes there may be good reasons for this. However, it may also be an outcome of gender biased assumptions. For instance, in evolutionary biology we commonly study sex differences and how these result from natural selection. We study this from both genetic and environmental perspectives. We may, however, often times assume that males and females have fixed trait abilities determined by their past evolutionary history, despite the fact that evolution is an ongoing process. Emanating from what is considered well known patterns, our assumptions may turn out to be biased by our expectations of gender. Examples from this can be found in sexual selection research where we increasingly find diversity in how this force operates on males and females, which can be contrasted with more simplistic views that “males compete and females care” as an evolutionary rule (in this context, it should be noted that the Bateman gradient paradigm has been under debate for quite some time). Furthermore, in medical and scientific textbooks, gendered assumptions have been identified and analyzed. For example, gendered views are apparent in textbooks when depicting egg, sperm, cervix, mucus, and semen, with active wordings for the male part and passive, objectified words for the female part — despite the fact that, scientifically, we know mucus activity is a vital part of the fertilization process. Similar gendered biases have also been found in sexual conflict studies. And when plasticity is found in male and female traits, there is a tendency to stick to firm sex differences despite contrary evidence – as, for example, in neuroscience. More recently we have seen in the news how, after DNA testing was conducted, the remains of a Viking leader were found to be those of a woman. This had been indicated previously but was not considered likely, probably due to pervasive gender biases. This provides an illustrative case of how researchers’ gender biased assumptions can affect scientific knowledge.