diff --git a/chainladder/core/correlation.py b/chainladder/core/correlation.py
index b00b21b4..dfa88981 100644
--- a/chainladder/core/correlation.py
+++ b/chainladder/core/correlation.py
@@ -324,10 +324,14 @@ def p_z_lower(z: int, n: int, p: float = 0.5) -> float:
             T = np.array(T)
             z_idx, n_idx = z.astype(int), n.astype(int)
             self.probs = T[z_idx, n_idx]
-            z_critical = triangle[triangle.valuation > triangle.valuation.min()]
-            # z_critical = z_critical[z_critical.development > z_critical.development.min()].dev_to_val().sum(
-            #     "origin") * 0
-            z_critical = z_critical.dev_to_val().dropna().sum("origin") * 0
+            # One column per link-ratio diagonal, labeled by ending valuation.
+            # Slicing by valuation (rather than dropna) keeps diagonals that are
+            # entirely NaN, which a triangle missing its earliest diagonals has:
+            # dropna() removed them while self.probs still carried one entry per
+            # diagonal, so the two disagreed and the DataFrame below raised
+            # "Shape of passed values is (1, 10), indices imply (1, 9)" (#320).
+            z_critical = triangle.dev_to_val().sum("origin")
+            z_critical = z_critical[z_critical.valuation > triangle.valuation.min()] * 0
             z_critical.values = np.array(self.probs) < p_critical
             z_critical.odims = triangle.odims[0:1]
             self.z_critical = z_critical
diff --git a/chainladder/core/tests/test_correlation.py b/chainladder/core/tests/test_correlation.py
index fba8d628..48430089 100644
--- a/chainladder/core/tests/test_correlation.py
+++ b/chainladder/core/tests/test_correlation.py
@@ -23,3 +23,13 @@ def test_dev_corr(raa: Triangle) -> None:
 def test_validate_critical(raa: Triangle) -> None:
     with pytest.raises(ValueError):
         raa.valuation_correlation(p_critical=1.5, total=True)
+
+
+def test_val_corr_incomplete_triangle(xyz: Triangle) -> None:
+    # GH #320: a triangle missing its earliest diagonals raised
+    # "Shape of passed values is (1, 10), indices imply (1, 9)" on repr,
+    # because z_critical dropped all-NaN diagonals while its values kept
+    # one entry per link-ratio diagonal.
+    z_critical = xyz["Paid"].valuation_correlation(p_critical=0.1, total=False).z_critical
+    assert z_critical.values.shape[-1] == len(z_critical.ddims)
+    assert repr(z_critical)