Revert "Do not zero out one-tailed z-statistics for p-values > 0.5"

docs/outputs.rst

@@ -33,6 +33,11 @@ Some of the values found in NiMARE include:
 - ``tau2``: Estimated between-study variance (IBMA only)
 - ``sigma2``: Estimated within-study variance (IBMA only)
 
+.. note::
+    For one-sided tests, p-values > 0.5 will have negative z-statistics. These values should not 
+    be confused with significant negative results. As a result, in NiMARE, these values are 
+    replaced by 0.
+
 Next, a series of key/value pairs describe the methods applied to generate the map.
 
 - ``desc``: Description of the data type. Only used when multiple maps with the same data type are produced by the same method.

nimare/tests/test_transforms.py

@@ -247,7 +247,7 @@ def test_ddimages_to_coordinates_merge_strategy(testdata_ibma):
 
 
 @pytest.mark.parametrize(
-    "expected_z,tail,expected_p",
+    "z,tail,expected_p",
     [
         (0.0, "two", 1.0),
         (0.0, "one", 0.5),
@@ -256,13 +256,10 @@ def test_ddimages_to_coordinates_merge_strategy(testdata_ibma):
         (-1.959963, "one", 0.975),
         (-1.959963, "two", 0.05),
         ([0.0, 1.959963, -1.959963], "two", [1.0, 0.05, 0.05]),
-        ([0.0, 1.959963, -1.959963], "one", [0.5, 0.025, 0.975]),
     ],
 )
-def test_z_to_p(expected_z, tail, expected_p):
-    """Test z to p, and p to z conversion."""
-    p = transforms.z_to_p(expected_z, tail)
-    z = transforms.p_to_z(expected_p, tail) * np.sign(expected_z)
+def test_z_to_p(z, tail, expected_p):
+    """Test z to p conversion."""
+    p = transforms.z_to_p(z, tail)
 
     assert np.all(np.isclose(p, expected_p))
-    assert np.all(np.isclose(z, expected_z))

nimare/transforms.py

@@ -678,13 +678,12 @@ def p_to_z(p, tail="two"):
         Z-statistics (unsigned)
     """
     p = np.array(p)
-
-    # Ensure that no p-values are converted to Inf/NaNs
-    p = np.clip(p, 1.0e-300, 1.0 - 1.0e-16)
     if tail == "two":
         z = stats.norm.isf(p / 2)
     elif tail == "one":
-        z = np.abs(stats.norm.isf(p))
+        z = stats.norm.isf(p)
+        z = np.array(z)
+        z[z < 0] = 0
     else:
         raise ValueError('Argument "tail" must be one of ["one", "two"]')