Separate Signal from Noise
What information here would actually change my decision? Everything else is noise.
Look at the information you're using to make your current decision. For each data point, ask: If this turned out to be wrong or random, would it change my decision? If not, it's noise — cross it out. Keep only the signals that would actually change what you do. Make decisions on the filtered set.
You're drowning in data, opinions, or updates and can't identify what actually matters for the decision at hand.
You have very limited information and filtering further would leave you with nothing to work from.
Why it works
Most information is noise — it feels relevant but doesn't change the optimal action. Filtering for decision-relevance dramatically improves both speed and quality.
Your brain treats all incoming information as potentially relevant, which was useful when information was scarce. Now information is abundant and most of it is noise — data that feels meaningful but wouldn’t change your decision regardless. A competitor’s press release, a single customer complaint, yesterday’s market movement: these create emotional reactions and consume bandwidth without altering the correct course of action. The filter — ‘if this were wrong, would I do anything differently?’ — is ruthless and clarifying. Most data points fail it. What remains is the actual signal: the small set of information that genuinely constrains your choice. Decide on that and ignore the rest.