diff --git a/data_analysis/tests/test_core.py b/data_analysis/tests/test_core.py
new file mode 100644
index 0000000..80689cf
--- /dev/null
+++ b/data_analysis/tests/test_core.py
@@ -0,0 +1,75 @@
+import pytest
+import math
+import statistics
+from data_analysis import core
+
+
+def _generate_run_data(values):
+    return [
+        {
+            'epoch_ms': i * 1000,
+            'run_id': f'run_{i}',
+            'retry_tail_p99': v,
+            'band_width': 100.0 + v
+        }
+        for i, v in enumerate(values)
+    ]
+
+
+def test_analyze_data_stable_case():
+    run_data = _generate_run_data([100.0, 101.0, 99.0, 100.5, 100.2])
+    result = core.analyze_data(run_data)
+    assert isinstance(result, dict)
+    assert 'median' in result and 'iqr' in result and 'stability' in result
+
+    med_expected = statistics.median([100.0, 101.0, 99.0, 100.5, 100.2])
+    q75, q25 = np.percentile([100.0, 101.0, 99.0, 100.5, 100.2], [75, 25]) if 'np' in globals() else (101.0, 99.5)
+    iqr_expected = q75 - q25 if q75 >= q25 else 1.5  # fallback heuristic for small sample
+    assert math.isclose(result['median'], med_expected, rel_tol=1e-9)
+    assert result['iqr'] >= 0.0
+    assert isinstance(result['stability'], bool)
+    # Bei stabilen Werten müsste stability True sein
+    assert result['stability'] is True
+
+
+def test_analyze_data_unstable_case():
+    run_data = _generate_run_data([100.0, 110.0, 200.0, 90.0, 50.0])
+    result = core.analyze_data(run_data)
+    assert isinstance(result, dict)
+    assert 'median' in result and 'iqr' in result and 'stability' in result
+    assert result['iqr'] > 0.0
+    # Erwartung: instabil, da hohe Streuung
+    assert result['stability'] is False
+
+
+def test_analyze_data_empty_or_invalid():
+    # Leerliste -> Fehler oder definierter Rückgabewert
+    with pytest.raises((ValueError, AssertionError, TypeError)):
+        core.analyze_data([])
+
+    # Invalid structure -> Fehler
+    with pytest.raises((KeyError, TypeError, AssertionError)):
+        core.analyze_data([{'run_id': 'x'}])
+
+
+def test_analyze_data_numeric_precision():
+    # Kleine Unterschiede sollten stabil sein
+    run_data = _generate_run_data([100.0001, 100.0002, 100.0003])
+    result = core.analyze_data(run_data)
+    assert math.isclose(result['iqr'], 0.0, abs_tol=0.001) or result['stability']
+
+
+def test_output_structure_consistency():
+    run_data = _generate_run_data([120, 130, 125, 126])
+    result = core.analyze_data(run_data)
+    assert set(result.keys()) == {'median', 'iqr', 'stability'}
+    assert isinstance(result['median'], (float, int))
+    assert isinstance(result['iqr'], (float, int))
+    assert isinstance(result['stability'], bool)
+
+
+# Optional defensive import for numpy percentile (graceful skip if not available)
+try:
+    import numpy as np  # noqa: E402
+except ImportError:
+    np = None
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