Establishing causation for a complex societal issue requires more than a single data set. The best approach is to build a "collage of evidence." This involves finding natural experiments—like states that enacted a policy before a national ruling—to test the hypothesis under different conditions and strengthen the causal claim.

Unlike scientific fields that build on previous discoveries, philosophy progresses cyclically. Each new generation must start fresh, grappling with the same fundamental questions of life and knowledge. This is why ancient ideas like Epicureanism reappear in modern forms like utilitarianism, as they address timeless human intuitions.

True scientific progress comes from being proven wrong. When an experiment falsifies a prediction, it definitively rules out a potential model of reality, thereby advancing knowledge. This mindset encourages researchers to embrace incorrect hypotheses as learning opportunities rather than failures, getting them closer to understanding the world.

A new scientific theory isn't valuable if it only recategorizes what we already know. Its true merit lies in suggesting an outrageous, unique, and testable experiment that no other existing theory could conceive of. Without this, it's just a reframing of old ideas.

The concept of shaping reality is universal, just packaged differently. A psychologist calls it self-image psychology, a scientist quantum physics, an atheist the placebo effect, and a Christian prayer. Understanding this allows skeptics to access the benefits of mindset work using a framework they trust.

Absolute truths are rare in complex systems like markets. A more pragmatic approach is to find guiding principles—like "buy assets for less than they're worth"—that are generally effective over the long term, even if they underperform in specific periods. This framework balances conviction with flexibility.

A two-step analytical method to vet information: First, distinguish objective (multi-source, verifiable) facts from subjective (opinion-based) claims. Second, assess claims on a matrix of probability and source reliability. A low-reliability source making an improbable claim, like many conspiracy theories, should be considered highly unlikely.

To counteract the brain's tendency to preserve existing conclusions, Charles Darwin deliberately considered evidence that contradicted his hypotheses. He was most rigorous when he felt most confident in an idea—a powerful, counterintuitive method for maintaining objectivity and avoiding confirmation bias.

Current LLMs fail at science because they lack the ability to iterate. True scientific inquiry is a loop: form a hypothesis, conduct an experiment, analyze the result (even if incorrect), and refine. AI needs this same iterative capability with the real world to make genuine discoveries.