--- a
+++ b/tests/preprocessing/test_bias.py
@@ -0,0 +1,110 @@
+import numpy as np
+import pandas as pd
+import pytest
+
+import ehrapy as ep
+from ehrapy.anndata._constants import CATEGORICAL_TAG, FEATURE_TYPE_KEY, NUMERIC_TAG
+
+
+@pytest.fixture
+def adata(rng):
+    corr = rng.integers(0, 100, 100)
+    df = pd.DataFrame(
+        {
+            "corr1": corr,
+            "corr2": corr * 2,
+            "corr3": corr * -1,
+            "contin1": rng.integers(0, 20, 50).tolist() + rng.integers(20, 40, 50).tolist(),
+            "cat1": [0] * 50 + [1] * 50,
+            "cat2": [10] * 10 + [11] * 40 + [10] * 30 + [11] * 20,
+        }
+    )
+    adata = ep.ad.df_to_anndata(df)
+    adata.var[FEATURE_TYPE_KEY] = [NUMERIC_TAG] * 4 + [CATEGORICAL_TAG] * 2
+    return adata
+
+
+def test_detect_bias_all_sensitive_features(adata):
+    results = ep.pp.detect_bias(
+        adata, "all", run_feature_importances=True, corr_method="spearman", feature_importance_threshold=0.4
+    )
+
+    assert "feature_correlations" in results.keys()
+    df = results["feature_correlations"]
+    assert len(df) == 4
+    assert df[(df["Feature 1"] == "corr1") & (df["Feature 2"] == "corr2")]["Spearman CC"].values[0] == 1
+    assert df[(df["Feature 1"] == "corr1") & (df["Feature 2"] == "corr3")]["Spearman CC"].values[0] == -1
+    assert df[(df["Feature 1"] == "corr2") & (df["Feature 2"] == "corr3")]["Spearman CC"].values[0] == -1
+    assert df[(df["Feature 1"] == "contin1") & (df["Feature 2"] == "cat1")]["Spearman CC"].values[0] > 0.5
+
+    assert "standardized_mean_differences" in results.keys()
+    df = results["standardized_mean_differences"]
+    assert len(df) == 4  # Both groups of cat1, cat2 respectively show a high SMD with contin1
+    smd_key = "Standardized Mean Difference"
+    assert df[(df["Sensitive Feature"] == "cat1") & (df["Sensitive Group"] == 0)][smd_key].values[0] < 0
+    assert df[(df["Sensitive Feature"] == "cat1") & (df["Sensitive Group"] == 1)][smd_key].values[0] > 0
+    assert df[(df["Sensitive Feature"] == "cat2") & (df["Sensitive Group"] == 10)][smd_key].values[0] > 0
+    assert df[(df["Sensitive Feature"] == "cat2") & (df["Sensitive Group"] == 11)][smd_key].values[0] < 0
+
+    assert "categorical_value_counts" in results.keys()
+    df = results["categorical_value_counts"]
+    assert len(df) == 2
+    assert df[(df["Sensitive Feature"] == "cat1") & (df["Sensitive Group"] == 0)]["Group 1 Percentage"].values[0] == 0.2
+    assert df[(df["Sensitive Feature"] == "cat1") & (df["Sensitive Group"] == 0)]["Group 2 Percentage"].values[0] == 0.8
+    assert (
+        df[(df["Sensitive Feature"] == "cat2") & (df["Sensitive Group"] == 10)]["Group 1 Percentage"].values[0] == 0.25
+    )
+    assert (
+        df[(df["Sensitive Feature"] == "cat2") & (df["Sensitive Group"] == 10)]["Group 2 Percentage"].values[0] == 0.75
+    )
+
+    assert "feature_importances" in results.keys()
+    df = results["feature_importances"]
+    assert len(df) >= 7  # 6 for the pairwise correlating features and one/two for contin1, which predicts cat1
+
+
+def test_detect_bias_specified_sensitive_features(adata):
+    results, result_adata = ep.pp.detect_bias(
+        adata,
+        ["contin1", "cat1"],
+        run_feature_importances=True,
+        corr_method="spearman",
+        feature_importance_threshold=0.5,
+        prediction_confidence_threshold=0.4,
+        copy=True,
+    )
+
+    assert "smd" not in adata.uns.keys()
+    assert "smd" in result_adata.uns.keys()
+
+    assert "feature_correlations" in results.keys()
+    df = results["feature_correlations"]
+    assert len(df) == 2  # cat1 & contin1 and contin1 & cat1
+    assert np.all(df["Spearman CC"] > 0.5)
+
+    assert "standardized_mean_differences" in results.keys()
+    df = results["standardized_mean_differences"]
+    assert len(df) == 2  # Both groups of cat1 show a high SMD with contin1
+    smd_key = "Standardized Mean Difference"
+    assert df[(df["Sensitive Feature"] == "cat1") & (df["Sensitive Group"] == 0)][smd_key].values[0] < -1
+    assert df[(df["Sensitive Feature"] == "cat1") & (df["Sensitive Group"] == 1)][smd_key].values[0] > 1
+
+    assert "categorical_value_counts" in results.keys()
+    df = results["categorical_value_counts"]
+    assert len(df) == 1
+    assert df[(df["Sensitive Feature"] == "cat1") & (df["Sensitive Group"] == 0)]["Group 1 Percentage"].values[0] == 0.2
+    assert df[(df["Sensitive Feature"] == "cat1") & (df["Sensitive Group"] == 0)]["Group 2 Percentage"].values[0] == 0.8
+
+    assert "feature_importances" in results.keys()
+    df = results["feature_importances"]
+    assert len(df) == 2  # contin1 predicts cat1 and cat1 predicts contin1
+
+
+def test_unencoded_data():
+    adata = ep.ad.df_to_anndata(
+        pd.DataFrame({"Unencoded": ["A", "B", "C", "D", "E", "F"], "Encoded": [1, 2, 3, 4, 5, 6]})
+    )
+    adata.var[FEATURE_TYPE_KEY] = [CATEGORICAL_TAG] * 2
+
+    with pytest.raises(ValueError):
+        ep.pp.detect_bias(adata, "all")