Causal Map

Four life lessons I learned while studying statistics: 1. False positives vs false negatives: Fewer false positives often come at the cost of more false negatives and vice versa. LIFE LESSON: The less stringent your criteria, the more crap you need to deal with, but the more stringent your criteria, the more genuinely good stuff you miss out on. Missed opportunities is the price of never wasting your time. Wasting your time is the price of never missing an opportunity. 2. Overfitting vs Underfitting: Less flexible models can't fit the data. More flexible models are prone to picking up patterns that don't generalize. LIFE LESSON: Over-thinking makes you more vulnerable to seeing patterns that aren't there. It turns you into a misinformation machine. 3. Bias vs Variance: More complex models give less consistent answers. Less complex models give more biased answers. LIFE LESSON: There is a natural trade-off between nuanced thought and consensus. People get mad when experts disagree especially when non-experts don't but that's actually what you would expect. 4. Curse of Dimensionality: Given a fixed amount of scenarios to learn from, there's a point beyond which considering additional factors stops helping. LIFE LESSON: If you haven't experienced much, keep things simple. Nuance without experience is actively harmful. SUMMARY: The common theme I see is these are cautionary tales for over-thinkers like me. There are hard mathematical limitations on what we can possibly know as rational beings. There is wisdom in knowing when to think but there's also wisdom in knowing when to stop.