{
  "example_kind": "original synthetic manual planning case",
  "slug": "forecast-absolute-versus-squared-error",
  "title": "Compare absolute and squared forecast errors",
  "scenario": "Synthetic planning case: Three duration forecasts are two days each. The observed toy outcomes are three, three and six days, giving errors one, one and four.",
  "layout": "evaluation",
  "dataset": {
    "heading": "Inspect the invented case records",
    "headers": [
      "Case",
      "Forecast days",
      "Actual days",
      "Absolute error",
      "Squared error"
    ],
    "rows": [
      [
        "A",
        "2",
        "3",
        "1",
        "1"
      ],
      [
        "B",
        "2",
        "3",
        "1",
        "1"
      ],
      [
        "C",
        "2",
        "6",
        "4",
        "16"
      ]
    ]
  },
  "derivation": "MAE=(1+1+4)/3=2 days. RMSE=√((1+1+16)/3)=√6≈2.45 days. Units return to days after taking the square root.",
  "result": "The invented sample has a two-day MAE and approximately 2.45-day RMSE. The squared-error measure gives the four-day miss more influence; neither number selects a universal objective.",
  "boundary": "The artifact exposes how one large miss changes two explicitly defined error summaries.",
  "limitations": "This tiny synthetic set is not a model comparison study. The rule and affected decision must be justified separately.",
  "sources": [
    "forecast"
  ],
  "tasks": [
    [
      "Match units and outcomes",
      "Evaluation owner",
      "All three errors describe the same duration target in days."
    ],
    [
      "Calculate both declared summaries",
      "Reviewer",
      "Absolute and squared contributions retain the large case-C miss."
    ],
    [
      "Choose the decision objective",
      "Planning lead",
      "The preferred loss rule is discussed in relation to actual consequences, not selected because its number looks lower."
    ]
  ],
  "faqs": [
    [
      "Can I directly compare an MAE number with RMSE to pick a model?",
      "They summarize different loss rules; compare models under the same declared objective and compatible cases."
    ],
    [
      "Why is the squared-error column in days squared?",
      "Squaring a duration changes units; RMSE takes a square root and returns to days."
    ]
  ],
  "method_references": [
    {
      "id": "forecast",
      "title": "Forecasting: Principles and Practice — accuracy",
      "url": "https://otexts.com/fpp3/accuracy.html",
      "scope": "Genuine held-out forecast and point-error evaluation context. Binary scoring examples use their own explicit toy definitions; no real task model is validated."
    }
  ]
}