diff --git a/pyerrors/fits.py b/pyerrors/fits.py
index d98f7aa0..8feb81fa 100644
--- a/pyerrors/fits.py
+++ b/pyerrors/fits.py
@@ -192,10 +192,10 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
     key_ls = sorted(list(xd.keys()))
 
     if sorted(list(yd.keys())) != key_ls:
-        raise Exception('x and y dictionaries do not contain the same keys.')
+        raise ValueError('x and y dictionaries do not contain the same keys.')
 
     if sorted(list(funcd.keys())) != key_ls:
-        raise Exception('x and func dictionaries do not contain the same keys.')
+        raise ValueError('x and func dictionaries do not contain the same keys.')
 
     x_all = np.concatenate([np.array(xd[key]) for key in key_ls])
     y_all = np.concatenate([np.array(yd[key]) for key in key_ls])
@@ -215,7 +215,7 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
         if not callable(funcd[key]):
             raise TypeError('func (key=' + key + ') is not a function.')
         if np.asarray(xd[key]).shape[-1] != len(yd[key]):
-            raise Exception('x and y input (key=' + key + ') do not have the same length')
+            raise ValueError('x and y input (key=' + key + ') do not have the same length')
         for i in range(100):
             try:
                 funcd[key](np.arange(i), x_all.T[0])
@@ -680,7 +680,7 @@ def fit_lin(x, y, **kwargs):
         out = least_squares(x, y, f, **kwargs)
         return out.fit_parameters
     else:
-        raise Exception('Unsupported types for x')
+        raise TypeError('Unsupported types for x')
 
 
 def qqplot(x, o_y, func, p, title=""):
diff --git a/tests/fits_test.py b/tests/fits_test.py
index 1af0e56e..92fffad5 100644
--- a/tests/fits_test.py
+++ b/tests/fits_test.py
@@ -713,15 +713,15 @@ def test_combined_fit_invalid_fit_functions():
 
 
 def test_combined_fit_invalid_input():
-    xvals =[]
-    yvals =[]
+    xvals = []
+    yvals = []
     err = 0.1
     def func_valid(a,x):
         return a[0] + a[1] * x
     for x in range(1, 8, 2):
         xvals.append(x)
         yvals.append(pe.pseudo_Obs(x + np.random.normal(0.0, err), err, 'test1') + pe.pseudo_Obs(0, err / 100, 'test2', samples=87))
-    with pytest.raises(Exception):
+    with pytest.raises(ValueError):
         pe.least_squares({'a':xvals}, {'b':yvals}, {'a':func_valid})
     with pytest.raises(Exception):
         pe.least_squares({'a':xvals}, {'a':yvals}, {'a':func_valid})