- People overestimate the impact of key individuals on an outcome (e.g. entrepreneurs, CEOs, politicians) by failing to account for other factors such as organizational culture, industry trends, economic climate, and so on.
- External factors (to an individual) can be considered luck - and they are very important.
- Linear regression when used properly is a simple but powerful tool for isolating the effect of something - however it only measures correlation not causality.
- Causation is much harder to prove than correlation. Experiments (e.g. a/b testing) are ideal but not always practical, particularly for macro decisions (e.g. new product launch, M&A)
- Complex outcomes (e.g. 5-year company growth, civil war in the Middle East) oftentimes have multiple causes and defy simple explanations.
- Plans are too optimistic because people do not account for potential unknown unknowns.
- Improve estimates by using reference points from similar cases. Modify your estimate based on these references by acknowledging differences from your current case with previous cases.
- Consider reverse causality. Does good company culture lead to good financial performance? Or does good financial performance help foster a good company culture?
- Consider confounding factors. Is there a third variable that affects both the independent and dependent variables you are looking at?
- Look for probabilistic distributions. Things are rarely surefire and once in a while failure is inevitable.
- Is there a worst case scenario that you haven't thought of yet? How undesirable is it? Is it so bad (e.g. the Great Recession) that once is one too many times?
- Does the organization as a whole not take enough risks due to incentives for individuals to avoid taking risks?
- Check for survivor bias. Are there instances that should be considered but are not because they are not well-known / out of business / etc?
- Is the halo effect causing you to subconsciously use the company's good financial performance to attribute positive beliefs on other aspects of the company (e.g. strategy, culture, people, etc)?
- Am I considering a point in time (e.g. cross-sectional) vs over time (e.g. longitudinal)? Studies over long periods of time are ideal. Asking individuals to recall events after the fact is likely to be affected with hindsight bias (i.e. risky decisions are seen as foolish / prescient given how things unfolded).
- People overestimate their ability to assess other's skills. Quantitatively assessing interview scores with post-hiring job reviews allows you to improve the hiring process in a systematic way.
- When analyzing the root cause of a failure, consider whether it was the failing of an individual (e.g. that person was irresponsible and should be fired) or the failing of a system (e.g. that mistake could have happened to any of us).
- Self-evaluate what lens / filters affects your perspective.
- Make decisions based on expected value when possible - it will slowly add up
- Use multiple disciplines when possible - think quantitatively and qualitatively.