{
  "artifact": "decisions_report",
  "direction": "ltr",
  "language": "en",
  "order_id": "00000000-0000-0000-0000-000000000101",
  "schema_version": 1,
  "sections": [
    {
      "id": "consultation_questions",
      "items": [],
      "markdown": "<a id=\"consultation_questions\"></a>\n\n## The consultation's questions and decisions\n\nNone recorded.",
      "title": "The consultation's questions and decisions"
    },
    {
      "id": "what_was_chosen",
      "items": [
        {
          "fields": {
            "hypothesis_id": "H1",
            "role_binding": {
              "x": "academic_self_efficacy",
              "y": "learning_persistence"
            },
            "test_id": "pearson_correlation"
          },
          "id": "what_was_chosen.1",
          "kind": "chosen_test"
        }
      ],
      "markdown": "<a id=\"what_was_chosen\"></a>\n\n## What was chosen\n\n- For hypothesis H1, the chosen analysis was pearson_correlation.",
      "title": "What was chosen"
    },
    {
      "id": "why",
      "items": [
        {
          "fields": {
            "hypothesis_id": "H1",
            "rule_path": [
              {
                "matched": true,
                "observed": "association",
                "predicate": "relationship==association"
              },
              {
                "matched": true,
                "observed": "2",
                "predicate": "n_content==2"
              },
              {
                "matched": true,
                "observed": "interval,interval",
                "predicate": "content.all_continuous"
              }
            ],
            "selected_rule_id": "assoc.pearson",
            "test_id": "pearson_correlation"
          },
          "id": "why.1",
          "kind": "selection_rationale"
        }
      ],
      "markdown": "<a id=\"why\"></a>\n\n## Why these choices were made\n\n- The choice of pearson_correlation for hypothesis H1 followed decision rule assoc.pearson.",
      "title": "Why these choices were made"
    },
    {
      "id": "alternatives_considered",
      "items": [
        {
          "fields": {
            "alternative_test_id": "spearman_correlation",
            "applied_test_id": "pearson_correlation",
            "hypothesis_id": "H1",
            "reason": "The data are interval/ratio-scaled, so the parametric default pearson_correlation is selected. The non-parametric equivalent spearman_correlation is the Story 1.7 assumption-ladder fallback, applied on normality/homogeneity FAILURE at execution time -- it is NOT chosen at selection (selection sees no assumption outcomes; FR22).",
            "reason_code": "parametric_default_preferred"
          },
          "id": "alternatives_considered.1",
          "kind": "rejected_alternative"
        }
      ],
      "markdown": "<a id=\"alternatives_considered\"></a>\n\n## Alternatives considered\n\n- hypothesis H1: Spearman correlation was kept as a backup. It is used only if your data do not meet the conditions Pearson correlation needs, which we check before the test is run.",
      "title": "Alternatives considered"
    },
    {
      "id": "multiplicity",
      "items": [],
      "markdown": "<a id=\"multiplicity\"></a>\n\n## Multiple-comparison correction\n\nNone recorded.",
      "title": "Multiple-comparison correction"
    },
    {
      "id": "robustness",
      "items": [],
      "markdown": "<a id=\"robustness\"></a>\n\n## What was tested for robustness\n\nNone recorded.",
      "title": "What was tested for robustness"
    },
    {
      "id": "data_preparation",
      "items": [
        {
          "fields": {
            "count": 0,
            "event_type": "missing.variable_analyzed",
            "no_op": false,
            "variable": "d1"
          },
          "id": "data_preparation.1",
          "kind": "missing_action"
        },
        {
          "fields": {
            "count": 0,
            "event_type": "missing.variable_analyzed",
            "no_op": false,
            "variable": "q1_1"
          },
          "id": "data_preparation.2",
          "kind": "missing_action"
        },
        {
          "fields": {
            "count": 0,
            "event_type": "missing.variable_analyzed",
            "no_op": false,
            "variable": "q1_2"
          },
          "id": "data_preparation.3",
          "kind": "missing_action"
        },
        {
          "fields": {
            "count": 0,
            "event_type": "missing.variable_analyzed",
            "no_op": false,
            "variable": "q1_3"
          },
          "id": "data_preparation.4",
          "kind": "missing_action"
        },
        {
          "fields": {
            "count": 0,
            "event_type": "missing.variable_analyzed",
            "no_op": false,
            "variable": "q1_4"
          },
          "id": "data_preparation.5",
          "kind": "missing_action"
        },
        {
          "fields": {
            "count": 0,
            "event_type": "missing.variable_analyzed",
            "no_op": false,
            "variable": "q1_5"
          },
          "id": "data_preparation.6",
          "kind": "missing_action"
        },
        {
          "fields": {
            "count": 0,
            "event_type": "missing.variable_analyzed",
            "no_op": false,
            "variable": "q2_1"
          },
          "id": "data_preparation.7",
          "kind": "missing_action"
        },
        {
          "fields": {
            "count": 0,
            "event_type": "missing.variable_analyzed",
            "no_op": false,
            "variable": "q2_2"
          },
          "id": "data_preparation.8",
          "kind": "missing_action"
        },
        {
          "fields": {
            "count": 0,
            "event_type": "missing.variable_analyzed",
            "no_op": false,
            "variable": "q2_3"
          },
          "id": "data_preparation.9",
          "kind": "missing_action"
        },
        {
          "fields": {
            "count": 0,
            "event_type": "missing.variable_analyzed",
            "no_op": false,
            "variable": "q2_4"
          },
          "id": "data_preparation.10",
          "kind": "missing_action"
        },
        {
          "fields": {
            "count": 0,
            "event_type": "missing.variable_analyzed",
            "no_op": false,
            "variable": "q2_5"
          },
          "id": "data_preparation.11",
          "kind": "missing_action"
        },
        {
          "fields": {
            "count": null,
            "event_type": "reliability.scale_analyzed",
            "no_op": false,
            "variable": null
          },
          "id": "data_preparation.12",
          "kind": "reliability_action"
        },
        {
          "fields": {
            "count": null,
            "event_type": "reliability.scale_reliable",
            "no_op": false,
            "variable": null
          },
          "id": "data_preparation.13",
          "kind": "reliability_action"
        },
        {
          "fields": {
            "count": null,
            "event_type": "reliability.scale_analyzed",
            "no_op": false,
            "variable": null
          },
          "id": "data_preparation.14",
          "kind": "reliability_action"
        },
        {
          "fields": {
            "count": null,
            "event_type": "reliability.scale_reliable",
            "no_op": false,
            "variable": null
          },
          "id": "data_preparation.15",
          "kind": "reliability_action"
        }
      ],
      "markdown": "<a id=\"data_preparation\"></a>\n\n## Data preparation\n\n- missing.variable_analyzed on d1\n- missing.variable_analyzed on q1_1\n- missing.variable_analyzed on q1_2\n- missing.variable_analyzed on q1_3\n- missing.variable_analyzed on q1_4\n- missing.variable_analyzed on q1_5\n- missing.variable_analyzed on q2_1\n- missing.variable_analyzed on q2_2\n- missing.variable_analyzed on q2_3\n- missing.variable_analyzed on q2_4\n- missing.variable_analyzed on q2_5\n- reliability.scale_analyzed on not recorded\n- reliability.scale_reliable on not recorded\n- reliability.scale_analyzed on not recorded\n- reliability.scale_reliable on not recorded",
      "title": "Data preparation"
    },
    {
      "id": "what_changed",
      "items": [],
      "markdown": "<a id=\"what_changed\"></a>\n\n## What changed along the way\n\nNone recorded.",
      "title": "What changed along the way"
    },
    {
      "id": "what_was_not_done",
      "items": [],
      "markdown": "<a id=\"what_was_not_done\"></a>\n\n## What was not done\n\nNone recorded.",
      "title": "What was not done"
    },
    {
      "id": "disclosures",
      "items": [
        {
          "fields": {
            "key": "ai_policy_not_verified"
          },
          "id": "disclosures.1",
          "kind": "disclosure"
        },
        {
          "fields": {
            "key": "software_framing"
          },
          "id": "disclosures.2",
          "kind": "disclosure"
        }
      ],
      "markdown": "<a id=\"disclosures\"></a>\n\n## Disclosures\n\n- Institutional AI-use policies were not verified. Statikali does not check whether your academic institution, journal, or supervisor permits the use of AI-assisted analysis tools. Verifying those policies and complying with them remains your responsibility.\n- An AI language model assisted only by writing the explanatory prose in this package. Every statistical computation, every number, and every test-selection decision was produced by a deterministic analysis engine — never by the AI.",
      "title": "Disclosures"
    }
  ]
}