Risk management has always required organizations to make decisions under uncertainty, and quantitative risk assessment represents one of the most powerful tools available for bringing structure and clarity to that uncertainty. At its core, quantitative risk assessment is the practice of using numerical data, statistical methods, and mathematical models to estimate the likelihood and potential impact of identified risks. Unlike qualitative approaches that rely on descriptive categories such as high, medium, or low, quantitative methods assign specific numerical values to risk parameters, enabling organizations to compare risks on a common scale, prioritize resource allocation with greater precision, and communicate risk exposures in terms that resonate with boards, investors, insurers, and regulators. The appeal of numbers is understandable. They offer an appearance of objectivity, they facilitate comparison, and they translate complex uncertainties into formats that can be incorporated into budgets, insurance applications, and strategic plans. Yet numbers also carry dangers that every Canadian organization should understand. When quantitative methods are applied poorly, when underlying data is unreliable, or when mathematical precision creates false confidence in inherently uncertain estimates, the resulting analysis can be worse than no analysis at all. This lesson explores when quantitative risk assessment genuinely helps organizations make better decisions and when it can mislead them into complacency or misallocated resources.