{
  "status": "shipping",
  "advantages": [],
  "limitations": [],
  "supersedes": [],
  "supersededBy": [],
  "firstSeen": null,
  "lastSeen": null,
  "updated": "2026-10-15",
  "citations": [
    {
      "id": "mahdisoltani2017",
      "title": "Mahdisoltani, Stefanovici and Schroeder. Improving Storage System Reliability with Proactive Error Prediction. USENIX ATC 2017, sections 4.1-4.3.",
      "url": "https://www.usenix.org/system/files/conference/atc17/atc17-mahdisoltani.pdf",
      "accessed": "2026-10-15"
    },
    {
      "id": "hesela-budget",
      "title": "Hesela: sector-error prediction and adaptive scrub-budget arithmetic.",
      "url": "https://hesela.com/analyses/sector-error-prediction-scrubbing/",
      "accessed": "2026-10-15"
    }
  ],
  "slug": "mean-time-to-detection",
  "term": "Mean time to detection",
  "aliases": [
    "MTTD"
  ],
  "definition": "Mean time to detection is the arithmetic mean of elapsed times between event occurrence and detection for a specified event population.",
  "note": "Define the event, timestamp origin and handling of undetected events. A detection timestamp alone does not reveal when a latent error began. Averaging only detected events can omit the longest delays; report censoring or the model used for unknown onset. This metric does not include subsequent repair. A baseline/adaptive MTTD ratio G gives fractional time reduction 1 - 1/G, not G - 1. [hesela-budget] The ATC 2017 scrub simulation evaluates mean detection delay. [mahdisoltani2017]",
  "related": [
    "latent-sector-error",
    "data-scrubbing",
    "adaptive-scrubbing",
    "right-censoring"
  ]
}