When selecting leadership, prioritize candidates with a consistent track record of success. Klinsky notes that what appears as "good luck" over a career is often the result of countless small, correct decisions that are hard to isolate but lead to positive outcomes.

During due diligence, it's crucial to look beyond returns. Top allocators analyze a manager's decision-making process, not just the outcome. They penalize managers who were “right for the wrong reasons” (luck) and give credit to those who were “wrong for the right reasons” (good process, bad luck).

A good outcome does not automatically validate the decision-making process, as luck plays a significant role. Howard Marks stresses the importance of intellectual humility in recognizing that a successful result could have stemmed from wrong reasons or randomness, a crucial distinction for repeatable success.

Like a farmer executing a six-month plan, focus on a repeatable, scientific process, knowing external factors can still affect the outcome. Ask "Was I unlucky or was I bad?" to avoid blaming your team for randomness and to improve the core process.

Advice from successful people is inherently flawed because it ignores the role of luck and timing. A more accurate approach is to study failures—the metaphorical planes that didn't return. Understanding why most people *don't* succeed provides a more robust framework for navigating risk than simply copying a survivor's path.

Leaders often fail to separate outcome from process. A good result from a bad decision (like a risky bet paying off) reinforces poor judgment. Attributing success solely to skill and failure to bad luck prevents process improvement and leads to repeated errors over time.

Critics claim explicit models for big decisions are flawed. However, relying on intuition is just using an opaque, implicit model you can't scrutinize. An explicit model, even if imperfect, makes assumptions transparent and challengeable, which is superior to a 'gut feeling' that cannot be dissected or debated.

It's tempting to think you can intuit the few factors a decision hinges on. This is often wrong. Complex systems have non-obvious leverage points. The process of building an explicit model reveals which variables have the most impact—a discovery you can't reliably make with intuition alone.

Known as "resulting," this bias makes it impossible to evaluate decisions fairly. We may deem a choice poor simply because it led to a loss, even if the process was sound. This prevents learning from probabilistic events and encourages chasing lucky outcomes instead of repeatable strategies.