# The Limits of Expert Judgment in Startup Evaluation Even experienced investors struggle to accurately assess early-stage ventures. - **Source:** [Strategic Entrepreneurship Journal](https://onlinelibrary.wiley.com/journal/1932443x) - **Published:** 2026-08-26 - **Topic:** [Finance](https://brifing.si/c/finance) - **Original article:** - **On Brifing:** https://brifing.si/s/a470a21b-c54b-4bbe-ba12-f3800e808b8b ## The Challenge of Early-Stage Evaluation Research analyzing over 1,600 startup screening decisions reveals the difficulty in predicting future potential. Weak and noisy observable signals make accurate assessments challenging for investors. The lack of financial data in early stages renders evaluations largely subjective. Even a 14-criteria evaluation system proves insufficient for definitive conclusions, highlighting the inherent uncertainty in venture assessment. ## The Gap Between Judgments and Actual Performance The study finds only weak correlation between evaluators' scores and startups' ultimate success. While criterion-based assessments successfully explain investors' overall recommendations, they fail to predict real success. Interestingly, some critical factors receive insufficient attention in decision-making, while trivial details exert disproportionate influence. This mismatch between perceived and actual importance of factors creates systemic evaluation errors. ## The Limited Advantage of Experience Experienced investors show only marginal advantages in the evaluation process. While veteran judges are better at filtering out weak ventures, they're only slightly better than novices at identifying high-potential startups. The research demonstrates that even a decade of experience doesn't create significant improvement in success prediction. These findings challenge the industry's traditional belief in experience as the ultimate differentiator. ## The Surprising Power of Objective Data The most striking finding shows that simple statistical models can predict as accurately as human investors. Models using only objective data from application forms match human judgment in accuracy. This points to the potential role of algorithmic tools in decision processes, especially in situations with high bias risk. Data-driven models can provide more objective assessments in inherently subjective evaluations. ## The Criteria Weighting Problem Evaluators systematically misweight assessment criteria. Factors critical for success receive insufficient attention, while less impactful features disproportionately influence decisions. For instance, team experience (a crucial factor) carries less weight than expected, while product design (a relatively superficial aspect) proves overly influential. This distortion reveals how investment committees can systematically err in their evaluations. ## Implications for the Industry The research offers important lessons for angel investing and incubation programs. It suggests traditional evaluation methods need revision, particularly the need for more data points in early-stage investing. Leading VCs are already experimenting with psychometric tests and behavioral economics to bridge this gap. Widespread adoption of such approaches could revolutionize how startups are evaluated and funded. ## Advice for Entrepreneurs Findings emphasize strategic approaches to application processes. Rather than focusing on factors investors notice but that have little actual impact, strengthening core value propositions proves wiser. For example, presenting real user interaction data might be more persuasive than perfecting pitch decks. Understanding evaluators' biases allows entrepreneurs to shape applications more effectively. ## An Inflection Point for Investment Ecosystem This study signals a potential paradigm shift in venture capital. Revealing the limits of human judgment underscores the need for alternative evaluation mechanisms. More scientific approaches could emerge, especially for early-stage investments. AI-assisted decision systems, diversified evaluation committees, and continuous performance monitoring may shape future investment decision processes. ## Sources covering this story - https://sms.onlinelibrary.wiley.com/doi/10.1002/sej.70042?af=R — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._