Signal and Noise

Claude Shannon formalized what engineers knew intuitively: communication is about distinguishing what’s intended from what’s accidental. Signal is the message you’re trying to send. Noise is everything else — interference, distortion, random variation. The ratio between them determines whether communication succeeds.

Signal-to-noise ratio is measured in decibels, but the concept extends beyond electronics. A scientific measurement has signal (the phenomenon) and noise (measurement error). A market has signal (fundamental value) and noise (random fluctuations). A conversation has signal (meaning) and noise (ambiguity, mishearing, distraction). The universal problem: extracting pattern from chaos.

Higher signal-to-noise requires either amplifying signal or suppressing noise. Averaging multiple measurements reduces random error. Redundancy in language (predictable patterns, context) helps you reconstruct garbled words. Error-correcting codes add structured redundancy so that damaged data can be recovered. compression works the opposite way: stripping redundancy because the receiver can reconstruct it.

The deeper insight: what counts as signal depends on your question. One person’s noise is another’s data. The astronomer filters out atmospheric distortion; the meteorologist studies it. The economist dismisses market micro-movements; the trader profits from them. Noise is signal you’re not interested in.

The challenge of modern life is too much information, most of it noise. Social media floods you with reactions you didn’t need to see. News cycles amplify rare events beyond their base rate. Data dashboards show every fluctuation, most of them meaningless. The skill isn’t finding information — it’s filtering it.

Nassim Taleb argues that more frequent data means more noise. Checking your portfolio hourly shows mostly random variation; checking yearly shows actual trend. The more granular the signal, the lower the ratio. Sometimes the best strategy is to receive less, not more.