As the industry embraces Artificial Intelligence and Machine Learning to lighten our load, we have learned that bias in the data models can impact our outcomes. In this session we are going to look at a type of bias that can sneak into our test automation paradigm without us knowing. Lucky for us, this bias can be beneficial if we know it's there and balance our testing pyramid to account for it. We'll take a deep dive into what confirmation bias is, how it can influence our automation testing, and how to balance that in other areas of our testing pyramid. We will examine the layers of the test pyramid and understand some of the flaws and biases that naturally come up in each layer. If we build our testing architecture in a balanced way, could the flaws and biases cancel each other out to make a highly effective test strategy?