diff --git a/tests/estimation/test_danish.py b/tests/estimation/test_danish.py
index 6d264393..344474af 100644
--- a/tests/estimation/test_danish.py
+++ b/tests/estimation/test_danish.py
@@ -50,45 +50,46 @@ def testGoodLstsqKwargs(self):
                 self.assertEqual(hist["lstsqResult"]["nfev"], 1)
 
     def testAccuracy(self):
-        # Create estimator
-        dan = DanishAlgorithm()
-        danBin = DanishAlgorithm(binning=2)
-
-        # Try several different random seeds
-        for seed in [12345, 23451, 34512, 45123, 51234]:
-            # Get the test data
-            zkTrue, intra, extra = forwardModelPair(seed=seed)
-
-            # Compute shape of binned images
-            shapex, shapey = intra.image.shape
-            binned_shapex = shapex // 2
-            binned_shapey = shapey // 2
-
-            # Ensure odd
-            if binned_shapex % 2 == 0:
-                binned_shapex -= 1
-            if binned_shapey % 2 == 0:
-                binned_shapey -= 1
-            binned_shape = (binned_shapex, binned_shapey)
-
-            # Test estimation with pairs and single donuts:
-            for images in [[intra, extra], [intra], [extra]]:
-                # Estimate Zernikes (in meters)
-                zkEst = dan.estimateZk(*images)
-
-                # Check that results are fairly accurate
-                self.assertLess(np.sqrt(np.sum((zkEst - zkTrue) ** 2)), 0.35e-6)
-
-                # Test with binning
-                zkEst = danBin.estimateZk(*images, saveHistory=True)
-                self.assertLess(np.sqrt(np.sum((zkEst - zkTrue) ** 2)), 0.35e-6)
-
-                # Test that we binned the images.
-                if "intra" in danBin.history:
-                    self.assertEqual(
-                        danBin.history["intra"]["image"].shape, binned_shape
-                    )
-                if "extra" in danBin.history:
-                    self.assertEqual(
-                        danBin.history["extra"]["image"].shape, binned_shape
-                    )
+        for jointFitPair in [True, False]:
+            # Create estimator
+            dan = DanishAlgorithm(jointFitPair=jointFitPair)
+            danBin = DanishAlgorithm(binning=2, jointFitPair=jointFitPair)
+
+            # Try several different random seeds
+            for seed in [12345, 23451, 34512, 45123]:
+                # Get the test data
+                zkTrue, intra, extra = forwardModelPair(seed=seed)
+
+                # Compute shape of binned images
+                shapex, shapey = intra.image.shape
+                binned_shapex = shapex // 2
+                binned_shapey = shapey // 2
+
+                # Ensure odd
+                if binned_shapex % 2 == 0:
+                    binned_shapex -= 1
+                if binned_shapey % 2 == 0:
+                    binned_shapey -= 1
+                binned_shape = (binned_shapex, binned_shapey)
+
+                # Test estimation with pairs and single donuts:
+                for images in [[intra, extra], [intra], [extra]]:
+                    # Estimate Zernikes (in meters)
+                    zkEst = dan.estimateZk(*images)
+
+                    # Check that results are fairly accurate
+                    self.assertLess(np.sqrt(np.sum((zkEst - zkTrue) ** 2)), 0.35e-6)
+
+                    # Test with binning
+                    zkEst = danBin.estimateZk(*images, saveHistory=True)
+                    self.assertLess(np.sqrt(np.sum((zkEst - zkTrue) ** 2)), 0.35e-6)
+
+                    # Test that we binned the images.
+                    if "intra" in danBin.history:
+                        self.assertEqual(
+                            danBin.history["intra"]["image"].shape, binned_shape
+                        )
+                    if "extra" in danBin.history:
+                        self.assertEqual(
+                            danBin.history["extra"]["image"].shape, binned_shape
+                        )