Skip to main content

Occam's Razor (Simplicity)

Occam’s Razor is a mental model that emphasizes simplicity in explanations and solutions. It states that when faced with competing hypotheses or explanations, the one with the fewest assumptions is usually the best choice.

What is Occam’s Razor?

Occam's Razor is a heuristic attributed to the medieval philosopher William of Ockham. It advises against unnecessary complexity by suggesting that simpler explanations are generally preferable to complex ones. It doesn’t guarantee the simplest solution is always correct, but it encourages focusing on explanations that require fewer speculative leaps.

Examples

  • Troubleshooting: If an app crashes unexpectedly, Occam’s Razor suggests starting by checking simple issues (such as resource leakage or recent updates) rather than immediately assuming complex underlying bugs or hardware failures.
  • Financial Markets: When a stock price suddenly falls, the simplest explanation (e.g., disappointing earnings or market-wide sell-offs) is typically more plausible than elaborate conspiracy theories or speculative market manipulations.
  • Feature Adoption: When users don’t engage with a newly launched feature, first consider simpler explanations such as poor discoverability or lack of clear instructions before concluding the feature itself isn’t valuable.

Connection to Bayesian Thinking

Occam’s Razor aligns naturally with Bayesian reasoning, where simpler hypotheses often have higher prior probabilities because they require fewer assumptions.

When evaluating competing hypotheses, Bayesian inference typically assigns a higher initial (prior) probability to simpler explanations, making them more likely to be correct unless additional evidence strongly favors a more complex alternative. Thus, Occam's Razor can be seen as a practical heuristic supporting Bayesian approaches to uncertainty and evidence evaluation.

note

Occam’s Razor is not an absolute rule. Sometimes complexity is necessary. However, it serves as a useful reminder that added complexity should be justified clearly by evidence or practical necessity rather than assumptions or speculation alone.