*2024-11-29* There is never enough data. This is not a temporary limitation, but a fundamental truth about complex systems. The moment you move beyond simple mechanical relationships into any domain involving multiple variables, human behavior, or systemic interactions, the amount of data required for certainty becomes impossible to obtain. Consider any significant decision: Is this career path right? Will this relationship work? Should we implement this policy? The variables involved are so numerous, their interactions so complex, that no amount of data collection could ever provide certainty. Even if we could gather perfect data, we lack the cognitive capacity to process it meaningfully. The academic world pretends otherwise. They produce studies showing correlations between two or three variables, acting as if this captures reality's complexity. But these studies are fundamentally inadequate - they can't account for the countless unmeasured variables, the flaws in methodology, the biases in interpretation. You can find data to support almost any position because reality is too complex to be captured in statistical relationships. This creates a peculiar form of dishonesty in how we approach decisions. We pretend we're being "data-driven" or "evidence-based" when we're actually pattern-matching based on limited information and personal biases. The more sophisticated we are, the better we become at dressing up our intuitions in the language of data and rationality. The path forward isn't gathering more data - it's acknowledging this fundamental uncertainty. Instead of pretending we can know enough to be certain, we must develop worldviews that work despite uncertainty. This means focusing on what actually matters: **predictive power**, **explanatory power**, and the **ability to achieve goals**. Experience becomes more valuable than studies because it gives us direct pattern-matching data about how reality actually works. Not filtered through methodologies and statistics, but raw feedback from reality itself. The key is remaining aware that even these patterns are provisional - useful models rather than absolute truths. This uncertainty isn't paralyzing once you accept it. Instead of seeking impossible certainty, we can focus on building models that help us navigate reality effectively. The test isn't whether we have enough data - we never will.