In our profession as testers, analysts, and developers, we try to base our decisions on objectivity. On data. Unfortunately, the information or data we base our decisions on is often biased. There are many reasons behind these biases, but the most striking one is a gender bias. A gender bias that is actually confirmed by a data bias. There are numerous examples of products being designed (IT and non-IT related), where a data bias results in a gender bias – with a negative effect on the quality, and on the uptake. Where does that put us as testers? What can we undertake to avoid releasing products that are not designed for the audience we are targeting?

This talk provides examples of the presence of data and gender biases, and how they result in negative consequences. I will also show you techniques to detect the biases, plus tools and best practices to avoid them.