The fastest skier isn't who wins, but the fastest among those who finish. Irreversible losses, like a career-ending injury, eliminate all future opportunities for gains. Therefore, over the long term, ensuring survival is mathematically more important than maximizing short-term performance, a concept known as ergodicity.
Maximizing daily output does not maximize yearly output. Long-term success requires investing in activities like building trust, relationships, or skills, which often yield no immediate returns and may seem inefficient day-to-day. Consistently choosing short-term tactics over long-term strategies ultimately limits growth.
Mathematical models like the Kelly Criterion are only as good as their inputs. Historical data, such as a stock market's return, isn't a fixed 'true' value but rather one random outcome from a distribution of possibilities. Using this single data point as a precise input leads to overconfidence and overallocation of capital.
When evaluating others' success, ask if their strategy would work for most people who adopt it, or if it relied heavily on luck. If a strategy isn't reproducible and leaves many casualties behind, it's not a model to be learned from, regardless of the impressive outlier outcome.
For an event with a 1-in-N chance of happening, if you try N times, the probability of it occurring at least once is roughly 63%. While this highlights the danger of repeated low-probability risks, it also applies positively. Consistently performing small, beneficial actions can compound to make eventual success almost a mathematical certainty.
Instead of relying on population averages for risk (e.g., car accidents), monitor your own close calls and mistakes. These 'near misses' are latent data points that provide a much better personal estimate of your true risk profile and how long you can last before a critical failure occurs if habits don't change.
People justify high-risk strategies by retroactively fitting themselves into a successful subgroup (e.g., 'Yes, most investors fail, but *smart* ones succeed, and I am smart'). This is 'hindsight gerrymandering'—using a trait like 'smartness,' which can only be proven after the fact, to create a biased sample and rationalize the risk.
In domains with extreme outcomes (music, startups), success is heavily influenced by luck, making it difficult to replicate. A more effective strategy is to study the common failure modes of the vast majority of talented people who tried. This provides a clearer roadmap of what to avoid than trying to copy a lucky winner.