diff --git a/pywhy_stats/fisherz.py b/pywhy_stats/fisherz.py
index 1e7ea8f..986289d 100644
--- a/pywhy_stats/fisherz.py
+++ b/pywhy_stats/fisherz.py
@@ -8,7 +8,7 @@
 from .p_value_result import PValueResult
 
 
-def ind(X: ArrayLike, Y: ArrayLike, correlation_matrix: Optional[ArrayLike] = None):
+def ind(X: ArrayLike, Y: ArrayLike, correlation_matrix: Optional[ArrayLike] = None) -> PValueResult:
     """Perform an independence test using Fisher-Z's test.
 
     Works on Gaussian random variables. This test is also known as the
@@ -38,7 +38,7 @@ def condind(
     Y: ArrayLike,
     condition_on: ArrayLike,
     correlation_matrix: Optional[ArrayLike] = None,
-):
+) -> PValueResult:
     """Perform an independence test using Fisher-Z's test.
 
     Works on Gaussian random variables. This test is also known as the
@@ -70,7 +70,7 @@ def _fisherz(
     Y: ArrayLike,
     condition_on: Optional[ArrayLike] = None,
     correlation_matrix: Optional[ArrayLike] = None,
-):
+) -> PValueResult:
     """Perform an independence test using Fisher-Z's test.
 
     Works on Gaussian random variables. This test is also known as the
@@ -111,6 +111,6 @@ def _fisherz(
     Z = 0.5 * log((1 + r) / (1 - r))
 
     # compute the test statistic
-    X = sqrt(sample_size - condition_on.shape[1] - 3) * abs(Z)
-    p = 2 * (1 - norm.cdf(abs(X)))
-    return PValueResult(X, p)
+    statistic = sqrt(sample_size - condition_on.shape[1] - 3) * abs(Z)
+    p = 2 * (1 - norm.cdf(abs(statistic)))
+    return PValueResult(statistic, p)
diff --git a/pywhy_stats/p_value_result.py b/pywhy_stats/p_value_result.py
index a1b11a8..60b5469 100644
--- a/pywhy_stats/p_value_result.py
+++ b/pywhy_stats/p_value_result.py
@@ -10,16 +10,16 @@ class PValueResult:
 
     Attributes
     ----------
-    p_value: float
+    pvalue : float
         The p-value represents the probability of observing the given test statistic, or more
         extreme results, under a certain null hypothesis.
-    test_statistic: float or numpy.ndarray or None
+    statistic : float or ArrayLike or None
         The test statistic of the hypothesis test, which might not always be available.
-    additional_information: object or None
+    additional_information : object or None
         Any additional information or metadata relevant to the specific test conducted. These could
         also be a state of the method to re-use it.
     """
 
-    p_value: float
-    test_statistic: Optional[Union[float, ArrayLike]] = None
+    pvalue: float
+    statistic: Optional[Union[float, ArrayLike]] = None
     additional_information: Optional[object] = None
diff --git a/tests/test_fisherz_test.py b/tests/test_fisherz_test.py
index f90a4f1..cc5e052 100644
--- a/tests/test_fisherz_test.py
+++ b/tests/test_fisherz_test.py
@@ -17,10 +17,10 @@ def test_fisherz_marg_ind():
     Y = X + X1 + 0.5 * rng.standard_normal((300, 1))
     Z = Y + 0.1 * rng.standard_normal((300, 1))
 
-    _, pvalue = fisherz.ind(X, X1)
-    assert pvalue > 0.05
-    _, pvalue = fisherz.ind(X, Z)
-    assert pvalue < 0.05
+    res = fisherz.ind(X, X1)
+    assert res.pvalue > 0.05
+    res = fisherz.ind(X, Z)
+    assert res.pvalue < 0.05
 
 
 @flaky.flaky(max_runs=3, min_passes=1)
@@ -36,9 +36,9 @@ def test_fisherz_cond_ind():
     Y = X + X1 + 0.5 * rng.standard_normal((300, 1))
     Z = Y + 0.1 * rng.standard_normal((300, 1))
 
-    _, pvalue = fisherz.condind(X, X1, Z)
-    assert pvalue < 0.05
-    _, pvalue = fisherz.condind(X, X1, Y)
-    assert pvalue < 0.05
-    _, pvalue = fisherz.condind(X, Z, Y)
-    assert pvalue > 0.05
+    res = fisherz.condind(X, X1, Z)
+    assert res.pvalue < 0.05
+    res = fisherz.condind(X, X1, Y)
+    assert res.pvalue < 0.05
+    res = fisherz.condind(X, Z, Y)
+    assert res.pvalue > 0.05