{
  "schema": "vedokrok.public-item.v1",
  "release_id": "MHC-RPUB-20260920-75ad787a",
  "url": "/knowledge/do-not-mistake-frequent-readings-for-independent-evidence",
  "id": "MHC-D-RESEARCH-0272",
  "version": "0.1.0",
  "title": "Do not mistake frequent readings for independent evidence",
  "summary": "Recording the same slow-moving process every second does not create a new world every second.",
  "kind": "question",
  "body": "Inspect whether successive readings depend on earlier ones. Autocorrelation can make an independent-error calculation too optimistic about uncertainty. Preserve timestamps and use a method appropriate to the time structure rather than treating the row count as the amount of independent information.",
  "limits": [
    "There is no universal conversion from raw rows to an effective independent sample count."
  ],
  "topics": [
    "union-statistical-judgment"
  ],
  "intents": [],
  "source_ids": [
    "RS-8528B19FF6D166F7"
  ],
  "evidence": [
    {
      "claim": "Autocorrelation indicates relationships among observations across time and can invalidate uncertainty calculations that assume independent errors.",
      "source_id": "RS-8528B19FF6D166F7",
      "role": "supports",
      "note": "A larger number of closely spaced readings is not automatically a proportionate increase in independent information.",
      "locator": "Lagged relationships; importance of checking randomness assumptions"
    }
  ],
  "use_when": [
    "A time series produces many closely spaced observations and very confident statistical claims."
  ],
  "avoid_when": [
    "There is no universal conversion from raw rows to an effective independent sample count."
  ],
  "example": "A room temperature logged every second contains many rows influenced by the same heating cycle.",
  "check": "The uncertainty calculation reflects the dependence structure rather than only the file's length.",
  "question": "How quickly can the measured process genuinely change? · Do adjacent readings share a trend, cycle or disturbance? · Does the analysis account for that dependence and the observation spacing?",
  "sources": [
    {
      "id": "RS-8528B19FF6D166F7",
      "title": "Autocorrelation",
      "url": "https://www.itl.nist.gov/div898/handbook/eda/section3/eda35c.htm"
    }
  ],
  "relations": [
    {
      "from": "MHC-D-RESEARCH-0272",
      "to": "MHC-D-RESEARCH-0263",
      "type": "useful_with",
      "url": "/knowledge/distinguish-variation-in-cases-from-uncertainty-in-a-mean"
    }
  ],
  "collections": [
    {
      "id": "RC-D4FD0B28897E9517",
      "title": "Check what a study can actually tell you",
      "url": "/collections/check-what-a-study-can-actually-tell-you"
    }
  ]
}