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Statistics experts highlight the difference between correlation and causation

The concept of uncorrelated dependence, where variables are related but not statistically correlated, is highlighted as a less appreciated aspect of statistical reasoning. This distinction is crucial in fields like machine learning and information theory, emphasizing that independence is a more stringent condition than simply having zero correlation. AI

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IMPACT Clarifies a fundamental statistical concept that underpins many machine learning algorithms and interpretations.

RANK_REASON This item discusses a statistical concept relevant to machine learning, presented as an opinion or observation rather than a research paper or model release.

Read on Mastodon — sigmoid.social →

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  1. Mastodon — sigmoid.social TIER_1 · [email protected] ·

    the inequivalence of correlation and causation is an oft-repeated maxim. what is less appreciated is that uncorrelated dependence is quite common: independence

    the inequivalence of correlation and causation is an oft-repeated maxim. what is less appreciated is that uncorrelated dependence is quite common: independence is a stronger restriction than zero correlation # stats # statistics # ML # informationTheory