diff --git a/03_quant_report.py b/03_quant_report.py
index 8ed06cc..7ffecdf 100644
--- a/03_quant_report.py
+++ b/03_quant_report.py
@@ -583,12 +583,6 @@ def _():
     return
 
 
-@app.cell
-def _(top3_voices_weighted):
-    print(top3_voices_weighted.head())
-    return
-
-
 @app.cell
 def _(S, top3_voices):
     S.plot_most_ranked_1(top3_voices, title="Most Popular Voice<br>(Number of Times Ranked 1st)", x_label='Voice')
@@ -607,112 +601,10 @@ def _():
 def _(S, data):
     # Get your voice scale data (from notebook)
     voice_1_10, _ = S.get_voice_scale_1_10(data)
+    S.plot_average_scores_with_counts(voice_1_10, x_label='Voice', domain=[1,10], title="Voice General Impression (Scale 1-10)")
     return (voice_1_10,)
 
 
-@app.cell(disabled=True)
-def _(S, voice_1_10):
-    S.plot_average_scores_with_counts(voice_1_10, x_label='Voice', domain=[1,10], title="Voice General Impression (Scale 1-10)")
-    return
-
-
-@app.cell(disabled=True)
-def _():
-    mo.md(r"""
-    ### Statistical Significance (Scale 1-10)
-    """)
-    return
-
-
-@app.cell
-def _(S, voice_1_10):
-    # Compute pairwise significance tests
-    pairwise_df, metadata = S.compute_pairwise_significance(
-        voice_1_10,
-        test_type="mannwhitney",  # or "ttest", "chi2", "auto"
-        alpha=0.05,
-        correction="bonferroni"   # or "holm", "none"
-    )
-
-    # View significant pairs
-    # print(pairwise_df.filter(pl.col('significant') == True))
-
-    # Create heatmap visualization
-    _heatmap = S.plot_significance_heatmap(
-        pairwise_df, 
-        metadata=metadata,
-        title="Voice Rating Significance<br>(Pairwise Comparisons)"
-    )
-
-    # Create summary bar chart
-    _summary = S.plot_significance_summary(
-        pairwise_df,
-        metadata=metadata
-    )
-
-    mo.md(f"""
-    {mo.ui.altair_chart(_heatmap)}
-
-    {mo.ui.altair_chart(_summary)}
-    """)
-    return
-
-
-@app.cell
-def _():
-    mo.md(r"""
-    ### Statistical Significance (Scale 1-10)
-    """)
-    return
-
-
-@app.cell
-def _(S, voice_1_10):
-    # Compute pairwise significance tests
-    pairwise_df, metadata = S.compute_pairwise_significance(
-        voice_1_10,
-        test_type="mannwhitney",  # or "ttest", "chi2", "auto"
-        alpha=0.05,
-        correction="bonferroni"   # or "holm", "none"
-    )
-
-    # View significant pairs
-    # print(pairwise_df.filter(pl.col('significant') == True))
-
-    # Create heatmap visualization
-    _heatmap = S.plot_significance_heatmap(
-        pairwise_df, 
-        metadata=metadata,
-        title="Voice Rating Significance<br>(Pairwise Comparisons)"
-    )
-
-    # Create summary bar chart
-    _summary = S.plot_significance_summary(
-        pairwise_df,
-        metadata=metadata
-    )
-
-    mo.md(f"""
-    {mo.ui.altair_chart(_heatmap)}
-
-    {mo.ui.altair_chart(_summary)}
-    """)
-    return
-
-
-@app.cell
-def _(S, data):
-    # Get your voice scale data (from notebook)
-    voice_1_10, _ = S.get_voice_scale_1_10(data)
-    return (voice_1_10,)
-
-
-@app.cell
-def _(S, voice_1_10):
-    S.plot_average_scores_with_counts(voice_1_10, x_label='Voice', domain=[1,10], title="Voice General Impression (Scale 1-10)")
-    return
-
-
 @app.cell(disabled=True)
 def _():
     mo.md(r"""
@@ -755,5 +647,10 @@ def _(S, voice_1_10):
     return
 
 
+@app.cell
+def _():
+    return
+
+
 if __name__ == "__main__":
     app.run()