diff --git a/packages/essreduce/src/ess/reduce/time_of_flight/resample.py b/packages/essreduce/src/ess/reduce/time_of_flight/resample.py
index 17612ff0..8f4b8b26 100644
--- a/packages/essreduce/src/ess/reduce/time_of_flight/resample.py
+++ b/packages/essreduce/src/ess/reduce/time_of_flight/resample.py
@@ -84,14 +84,15 @@ def rebin_strictly_increasing(da: sc.DataArray, dim: str) -> sc.DataArray:
     da = da.rename_dims({da.coords[dim].dim: dim})
     slices = find_strictly_increasing_sections(da.coords[dim])
     if len(slices) == 1:
-        return da[dim, slices[0]]
+        # Slices refer to the indices in the coord, which are bin edges.
+        # For slicing data we need to stop at the last index minus one.
+        return da[dim, slices[0].start : slices[0].stop - 1]
     if not slices:
         raise ValueError("No strictly increasing sections found.")
     if da.coords[dim].dtype not in (sc.DType.float64, sc.DType.float32):
         # rebin does not like integer coords.
         da = da.assign_coords({dim: da.coords[dim].to(dtype='float64')})
-    # Slices refer to the indices in the coord, which are bin edges. For slicing data
-    # we need to stop at the last index minus one.
+    # Same -1 as above in the len(slices) == 1 case.
     sections = [da[dim, section.start : section.stop - 1] for section in slices]
     edges = make_regular_grid(da.coords[dim], dim=dim, slices=slices)
     return sc.reduce([sc.rebin(section, {dim: edges}) for section in sections]).sum()
diff --git a/packages/essreduce/tests/time_of_flight/resample_tests.py b/packages/essreduce/tests/time_of_flight/resample_test.py
similarity index 91%
rename from packages/essreduce/tests/time_of_flight/resample_tests.py
rename to packages/essreduce/tests/time_of_flight/resample_test.py
index 3984d166..6d54c5f9 100644
--- a/packages/essreduce/tests/time_of_flight/resample_tests.py
+++ b/packages/essreduce/tests/time_of_flight/resample_test.py
@@ -321,6 +321,51 @@ def test_with_single_increasing_section(self):
         # For a single increasing section, should return just that section
         assert sc.identical(result, da)
 
+    def test_with_single_increasing_section_decreasing_last(self):
+        tof = sc.array(dims=['tof'], values=[1, 2, 3, 4, 5, 4])
+        data = sc.array(dims=['tof'], values=[10, 20, 30, 40, 50])
+        da = sc.DataArray(data=data, coords={'tof': tof})
+
+        result = resample.rebin_strictly_increasing(da, 'tof')
+
+        # Check the rebinned result has a regular grid from 1 to 5
+        expected_tof = sc.array(dims=['tof'], values=[1, 2, 3, 4, 5])
+        assert_identical(result.coords['tof'], expected_tof)
+
+        # Check the data values are sliced
+        expected_data = sc.array(dims=['tof'], values=[10, 20, 30, 40])
+        assert_identical(result.data, expected_data)
+
+    def test_with_single_increasing_section_nan_at_end(self):
+        tof = sc.array(dims=['tof'], values=[1, 2, 3, 4, 5, np.nan])
+        data = sc.array(dims=['tof'], values=[10, 20, 30, 40, 50])
+        da = sc.DataArray(data=data, coords={'tof': tof})
+
+        result = resample.rebin_strictly_increasing(da, 'tof')
+
+        # Check the rebinned result has a regular grid from 1 to 5
+        expected_tof = sc.array(dims=['tof'], values=[1.0, 2, 3, 4, 5])
+        assert_identical(result.coords['tof'], expected_tof)
+
+        # Check the data values are sliced
+        expected_data = sc.array(dims=['tof'], values=[10, 20, 30, 40])
+        assert_identical(result.data, expected_data)
+
+    def test_with_single_increasing_section_2_nans_at_end(self):
+        tof = sc.array(dims=['tof'], values=[1, 2, 3, 4, np.nan, np.nan])
+        data = sc.array(dims=['tof'], values=[10, 20, 30, 40, 50])
+        da = sc.DataArray(data=data, coords={'tof': tof})
+
+        result = resample.rebin_strictly_increasing(da, 'tof')
+
+        # Check the rebinned result has a regular grid from 1 to 4
+        expected_tof = sc.array(dims=['tof'], values=[1.0, 2, 3, 4])
+        assert_identical(result.coords['tof'], expected_tof)
+
+        # Check the data values are sliced
+        expected_data = sc.array(dims=['tof'], values=[10, 20, 30])
+        assert_identical(result.data, expected_data)
+
     def test_with_single_section_minimum_length(self):
         """Test with a single section of the minimum length (2 points)."""
         tof = sc.array(dims=['tof'], values=[1, 2])