In many government and private sectors, employee promotions often rely on a dual process: interviews and internal performance assessments. While interviews are generally fair due to the anonymity of committee members, the internal assessments conducted by immediate superiors tend to be subjective and occasionally biased. This creates a significant disparity where some non-performing employees receive high assessment scores, while diligent workers may receive lower scores, ultimately affecting their promotion opportunities. This paper introduces a Statistical normalization model to address these disparities in performance evaluation scores. The model ensures comparability across different evaluators and departments by standardizing the scores before they are combined with interview marks. The final aggregated scores form the basis for shortlisting candidates for promotion. Using three distinct datasets representing varying performance levels, this case study demonstrates that normalization results in datasets with identical statistical properties, making performance evaluations more equitable, consistent, and data-driven.