Recency bias, a concept prevalent in psychology, behavioral economics, and data analysis, refers to the tendency to focus on the most recent data while disregarding historical information, potentially skewing statistical analysis and prediction models. This bias can lead analysts to make decisions based on recent trends without considering the broader context, as illustrated by examples in the stock market and digital marketing. To counteract recency bias, it is essential to consider the entire dataset, test for statistical significance, and remain aware of narrative fallacies. Beyond recency bias, the text touches on CUPED, an approach that accelerates experiments with less bias, and highlights insights from experts like Ronny Kohavi and Allon Korem on fostering a robust experimentation culture. It also mentions the evolution of platforms like Optimizely and the impact of A/B testing on decision-making, drawing from experiences at companies like Statsig and Facebook. Overall, the emphasis is on the importance of balanced data analysis, recognizing both recent and historical data to avoid misleading conclusions.