Cognitive Neuroscience aims to establish links between brain activity and behavior, but with limited resources, experiments rely on sample data to draw conclusions. However, even with careful analysis, there is always a risk of Type I errors, where significant patterns may appear by chance. This blog post delves into the significance threshold, p-values, and the pitfalls researchers face, shedding light on how data preprocessing can inadvertently amplify the chances of false discoveries in neuroscience research.