Mitigating Cognitive Biases in Investment Decision-Making: A Statistical Approach to Enhancing Data Representation Integrity

This study investigates the impact of cognitive biases on investment decision-making and proposes a statistical framework for their mitigation. We hypothesized that confirmation, anchoring, and recency biases significantly influence investment outcomes. Utilizing machine learning algorithms and Bayesian inference, we analyzed a five-year dataset of investment decisions. Results revealed that these biases collectively affected 38% of decisions, with confirmation bias being most prevalent. Our proposed framework, incorporating real-time statistical checks and automated alerts, demonstrated a 77% reduction in biased decision-making in a controlled experiment. Preliminary outcome analysis indicates a positive correlation between bias mitigation and investment performance.