diff --git a/run_data_analysis/tests/test_core.py b/run_data_analysis/tests/test_core.py
new file mode 100644
index 0000000..3521ac1
--- /dev/null
+++ b/run_data_analysis/tests/test_core.py
@@ -0,0 +1,64 @@
+import pytest
+import math
+from run_data_analysis import core
+
+@pytest.fixture
+def valid_run_data():
+    return {
+        "band_center": 125.4,
+        "band_width": 20.1,
+        "cluster_score": 0.85,
+        "retry_tail": 1.12
+    }
+
+@pytest.fixture
+def edge_run_data():
+    # Enthält extreme, aber valide Werte zur Stabilitätsprüfung
+    return {
+        "band_center": 0.0,
+        "band_width": 0.0001,
+        "cluster_score": 0.0,
+        "retry_tail": 10.0
+    }
+
+@pytest.fixture
+def invalid_run_data():
+    # Fehlende Felder, um Validierung zu testen
+    return {
+        "band_center": "unknown",
+        "band_width": None,
+        "cluster_score": 0.9
+    }
+
+def test_analyse_run_data_returns_expected_keys(valid_run_data):
+    result = core.analyse_run_data(valid_run_data)
+    expected_keys = {"band_center", "band_width", "cluster_score", "retry_tail", "aggregates"}
+    assert isinstance(result, dict)
+    assert expected_keys.issubset(result.keys())
+
+def test_analyse_run_data_value_types(valid_run_data):
+    result = core.analyse_run_data(valid_run_data)
+    # Prüfe auf numerische Ergebnisse
+    for key in ["band_center", "band_width", "cluster_score", "retry_tail"]:
+        assert isinstance(result[key], (int, float))
+        assert not math.isnan(result[key])
+
+
+def test_analyse_run_data_edge_values(edge_run_data):
+    result = core.analyse_run_data(edge_run_data)
+    assert result["band_center"] == pytest.approx(0.0)
+    assert result["band_width"] > 0
+    assert result["retry_tail"] >= 10.0
+
+def test_analyse_run_data_invalid_input_raises(invalid_run_data):
+    with pytest.raises((TypeError, ValueError)):
+        core.analyse_run_data(invalid_run_data)
+
+def test_analyse_run_data_aggregate_metrics(valid_run_data):
+    result = core.analyse_run_data(valid_run_data)
+    # Aggregates sollte statistische Substruktur enthalten
+    aggregates = result.get("aggregates")
+    assert isinstance(aggregates, dict)
+    for metric in ("mean", "stdev"):
+        assert metric in aggregates
+    assert aggregates["mean"] > 0.0
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