diff --git a/src/cabinetry/fit/__init__.py b/src/cabinetry/fit/__init__.py
index b4118da0..0029c4e5 100644
--- a/src/cabinetry/fit/__init__.py
+++ b/src/cabinetry/fit/__init__.py
@@ -367,7 +367,11 @@ def _run_minos(
 
 
 def _goodness_of_fit(
-    model: pyhf.pdf.Model, data: List[float], best_twice_nll: float
+    model: pyhf.pdf.Model,
+    data: List[float],
+    best_twice_nll: float,
+    *,
+    fix_pars: Optional[List[bool]] = None,
 ) -> float:
     """Calculates goodness-of-fit p-value with a saturated model.
 
@@ -381,6 +385,8 @@ def _goodness_of_fit(
         data (List[float]): the observed data
         best_twice_nll (float): best-fit -2 log(likelihood) of fit for which goodness-
             of-fit should be calculated
+        fix_pars (Optional[List[bool]], optional): list of booleans specifying which
+            parameters are held constant, defaults to None (use ``pyhf`` suggestion)
 
     Returns:
         float: goodness-of-fit p-value
@@ -416,7 +422,7 @@ def _goodness_of_fit(
     # of bins minus the number of unconstrained parameters
     n_dof = sum(
         model.config.channel_nbins.values()
-    ) - model_utils.unconstrained_parameter_count(model)
+    ) - model_utils.unconstrained_parameter_count(model, fix_pars=fix_pars)
     log.debug(f"number of degrees of freedom: {n_dof}")
 
     if n_dof <= 0:
@@ -502,7 +508,9 @@ def fit(
 
     if goodness_of_fit:
         # calculate goodness-of-fit with saturated model
-        p_val = _goodness_of_fit(model, data, fit_results.best_twice_nll)
+        p_val = _goodness_of_fit(
+            model, data, fit_results.best_twice_nll, fix_pars=fix_pars
+        )
         fit_results = fit_results._replace(goodness_of_fit=p_val)
 
     return fit_results
diff --git a/src/cabinetry/model_utils.py b/src/cabinetry/model_utils.py
index ec6533f8..a6a4cadc 100644
--- a/src/cabinetry/model_utils.py
+++ b/src/cabinetry/model_utils.py
@@ -481,8 +481,12 @@ def prediction(
     )
 
 
-def unconstrained_parameter_count(model: pyhf.pdf.Model) -> int:
-    """Returns the number of unconstrained parameters in a model.
+def unconstrained_parameter_count(
+    model: pyhf.pdf.Model,
+    *,
+    fix_pars: Optional[List[bool]] = None,
+) -> int:
+    """Returns the number of unconstrained, non-constant parameters in a model.
 
     The number is the sum of all independent parameters in a fit. A shapefactor that
     affects multiple bins enters the count once for each independent bin. Parameters
@@ -490,15 +494,23 @@ def unconstrained_parameter_count(model: pyhf.pdf.Model) -> int:
 
     Args:
         model (pyhf.pdf.Model): model to count parameters for
+        fix_pars (Optional[List[bool]], optional): list of booleans specifying which
+            parameters are held constant, defaults to None (use ``pyhf`` suggestion)
 
     Returns:
         int: number of unconstrained parameters
     """
     n_pars = 0
+    # custom fixed parameters overrule suggested fixed parameters
+    if fix_pars is None:
+        fix_pars = model.config.suggested_fixed()
+
     for parname in model.config.par_order:
         if not model.config.param_set(parname).constrained:
             # only consider non-constant parameters
-            n_pars += model.config.param_set(parname).suggested_fixed.count(False)
+            par_slice = model.config.par_slice(parname)
+            n_pars += fix_pars[par_slice].count(False)
+
     return n_pars
 
 
diff --git a/tests/fit/test_fit.py b/tests/fit/test_fit.py
index c00d0651..16064201 100644
--- a/tests/fit/test_fit.py
+++ b/tests/fit/test_fit.py
@@ -353,7 +353,7 @@ def test__goodness_of_fit(
     assert mock_pars.call_args[1] == {}
     assert mock_count.call_count == 1
     assert mock_count.call_args[0][0].spec == model.spec
-    assert mock_count.call_args[1] == {}
+    assert mock_count.call_args[1] == {"fix_pars": None}
     assert "Delta NLL = 0.084185" in [rec.message for rec in caplog.records]
     assert np.allclose(p_val, 0.91926079)
     caplog.clear()
@@ -372,7 +372,7 @@ def test__goodness_of_fit(
     assert mock_pars.call_count == 2  # no new call (no auxdata)
     assert mock_count.call_count == 3
     assert mock_count.call_args[0][0].spec == model.spec
-    assert mock_count.call_args[1] == {}
+    assert mock_count.call_args[1] == {"fix_pars": None}
     assert (
         "cannot calculate p-value: 0 degrees of freedom and Delta NLL = 0.000000"
         in [rec.message for rec in caplog.records]
@@ -480,7 +480,8 @@ def test_fit(mock_fit, mock_print, mock_gof):
 
     # goodness-of-fit test
     fit_results_gof = fit.fit(model, data, goodness_of_fit=True)
-    assert mock_gof.call_args_list == [((model, data, 2.0), {})]
+    assert mock_gof.call_args[0] == (model, data, 2.0)
+    assert mock_gof.call_args[1] == {"fix_pars": None}
     assert fit_results_gof.goodness_of_fit == 0.1
 
 
diff --git a/tests/test_model_utils.py b/tests/test_model_utils.py
index b1369d6d..b74ef481 100644
--- a/tests/test_model_utils.py
+++ b/tests/test_model_utils.py
@@ -368,6 +368,12 @@ def test_unconstrained_parameter_count(example_spec, example_spec_shapefactor):
     model = pyhf.Workspace(example_spec_shapefactor).model()
     assert model_utils.unconstrained_parameter_count(model) == 3
 
+    # fixed parameters can be provided to function
+    model = pyhf.Workspace(example_spec_shapefactor).model()
+    fix_pars = model.config.suggested_fixed()
+    fix_pars[0] = True
+    assert model_utils.unconstrained_parameter_count(model, fix_pars=fix_pars) == 2
+
     # fixed parameters are skipped in counting
     example_spec_shapefactor["measurements"][0]["config"]["parameters"].append(
         {"name": "Signal strength", "fixed": True}