diff --git a/src/main/java/ca/joeltherrien/randomforest/Main.java b/src/main/java/ca/joeltherrien/randomforest/Main.java
index 9ff7593..f9b7194 100644
--- a/src/main/java/ca/joeltherrien/randomforest/Main.java
+++ b/src/main/java/ca/joeltherrien/randomforest/Main.java
@@ -100,13 +100,6 @@ public class Main {
             final CompetingRiskErrorRateCalculator errorRateCalculator = new CompetingRiskErrorRateCalculator(dataset, forest, useBootstrapPredictions);
             final PrintWriter printWriter = new PrintWriter(settings.getSaveTreeLocation() + "/errors.txt");
 
-            System.out.println("Running Naive Mortality");
-
-            final double naiveMortality = errorRateCalculator.calculateNaiveMortalityError(events);
-            printWriter.write("Naive Mortality: ");
-            printWriter.write(Double.toString(naiveMortality));
-            printWriter.write('\n');
-
             System.out.println("Running Naive Concordance");
 
             final double[] naiveConcordance = errorRateCalculator.calculateConcordance(events);
diff --git a/src/main/java/ca/joeltherrien/randomforest/responses/competingrisk/CompetingRiskErrorRateCalculator.java b/src/main/java/ca/joeltherrien/randomforest/responses/competingrisk/CompetingRiskErrorRateCalculator.java
index a837f0e..4f6f3fe 100644
--- a/src/main/java/ca/joeltherrien/randomforest/responses/competingrisk/CompetingRiskErrorRateCalculator.java
+++ b/src/main/java/ca/joeltherrien/randomforest/responses/competingrisk/CompetingRiskErrorRateCalculator.java
@@ -31,54 +31,6 @@ public class CompetingRiskErrorRateCalculator {
 
     }
 
-    /**
-     * Idea for this error rate; go through every observation I have and calculate its mortality for the different events. If the event with the highest mortality is not the one that happened,
-     * then we add one to the error scale.
-     *
-     * Ignore censored observations.
-     *
-     * Possible extensions might involve counting how many other events had higher mortality, instead of just a single PASS / FAIL.
-     *
-     * My observation is that this error rate isn't very useful...
-     *
-     * @return
-     */
-    public double calculateNaiveMortalityError(final int[] events){
-        int failures = 0;
-        int attempts = 0;
-
-        response_loop:
-        for(int i=0; i<dataset.size(); i++){
-            final CompetingRiskResponse response = dataset.get(i).getResponse();
-
-            if(response.isCensored()){
-                continue;
-            }
-            attempts++;
-
-            final CompetingRiskFunctions functions = riskFunctions.get(i);
-            final int delta = response.getDelta();
-            final double time = response.getU();
-            final double shouldBeHighestMortality = functions.calculateEventSpecificMortality(delta, time);
-
-            for(final int event : events){
-                if(event != delta){
-                    final double otherEventMortality = functions.calculateEventSpecificMortality(event, time);
-
-                    if(shouldBeHighestMortality < otherEventMortality){
-                        failures++;
-                        continue response_loop;
-                    }
-
-                }
-            }
-        }
-
-        return (double) failures / (double) attempts;
-
-
-    }
-
 
     public double[] calculateConcordance(final int[] events){
         final double tau = dataset.stream().mapToDouble(row -> row.getResponse().getU()).max().orElse(0.0);
diff --git a/src/test/java/ca/joeltherrien/randomforest/competingrisk/TestCompetingRisk.java b/src/test/java/ca/joeltherrien/randomforest/competingrisk/TestCompetingRisk.java
index e38ce57..ea2d7dd 100644
--- a/src/test/java/ca/joeltherrien/randomforest/competingrisk/TestCompetingRisk.java
+++ b/src/test/java/ca/joeltherrien/randomforest/competingrisk/TestCompetingRisk.java
@@ -244,8 +244,6 @@ public class TestCompetingRisk {
 
         closeEnough(0.452, errorRates[0], 0.02);
         closeEnough(0.446, errorRates[1], 0.02);
-
-        System.out.println(errorRateCalculator.calculateNaiveMortalityError(new int[]{1,2}));
     }
 
     @Test
@@ -326,8 +324,6 @@ public class TestCompetingRisk {
         closeEnough(0.395, errorRates[0], 0.02);
         closeEnough(0.345, errorRates[1], 0.02);
 
-        System.out.println(errorRateCalculator.calculateNaiveMortalityError(new int[]{1,2}));
-
     }
 
 }
diff --git a/src/test/java/ca/joeltherrien/randomforest/competingrisk/TestCompetingRiskErrorRateCalculator.java b/src/test/java/ca/joeltherrien/randomforest/competingrisk/TestCompetingRiskErrorRateCalculator.java
index 45d0928..be8d9da 100644
--- a/src/test/java/ca/joeltherrien/randomforest/competingrisk/TestCompetingRiskErrorRateCalculator.java
+++ b/src/test/java/ca/joeltherrien/randomforest/competingrisk/TestCompetingRiskErrorRateCalculator.java
@@ -47,61 +47,5 @@ public class TestCompetingRiskErrorRateCalculator {
 
     }
 
-    @Test
-    public void testNaiveMortality(){
-        final CompetingRiskResponse response1 = new CompetingRiskResponse(1, 5.0);
-        final CompetingRiskResponse response2 = new CompetingRiskResponse(0, 6.0);
-        final CompetingRiskResponse response3 = new CompetingRiskResponse(2, 8.0);
-        final CompetingRiskResponse response4 = new CompetingRiskResponse(1, 3.0);
-
-        final List<Row<CompetingRiskResponse>> dataset = Utils.easyList(
-                new Row<>(new Covariate.Value[]{}, 1, response1),
-                new Row<>(new Covariate.Value[]{}, 2, response2),
-                new Row<>(new Covariate.Value[]{}, 3, response3),
-                new Row<>(new Covariate.Value[]{}, 4, response4)
-        );
-
-        final double[] mortalityOneArray = new double[]{1, 4, 3, 9};
-        final double[] mortalityTwoArray = new double[]{2, 3, 4, 7};
-
-        // response1 was predicted incorrectly
-        // response2 doesn't matter; censored
-        // response3 was correctly predicted
-        // response4 was correctly predicted
-
-        // Expect 1/3 for my error
-
-        final CompetingRiskFunctions function1 = mock(CompetingRiskFunctions.class);
-        when(function1.calculateEventSpecificMortality(1, response1.getU())).thenReturn(mortalityOneArray[0]);
-        when(function1.calculateEventSpecificMortality(2, response1.getU())).thenReturn(mortalityTwoArray[0]);
-
-        final CompetingRiskFunctions function2 = mock(CompetingRiskFunctions.class);
-        when(function2.calculateEventSpecificMortality(1, response2.getU())).thenReturn(mortalityOneArray[1]);
-        when(function2.calculateEventSpecificMortality(2, response2.getU())).thenReturn(mortalityTwoArray[1]);
-
-        final CompetingRiskFunctions function3 = mock(CompetingRiskFunctions.class);
-        when(function3.calculateEventSpecificMortality(1, response3.getU())).thenReturn(mortalityOneArray[2]);
-        when(function3.calculateEventSpecificMortality(2, response3.getU())).thenReturn(mortalityTwoArray[2]);
-
-        final CompetingRiskFunctions function4 = mock(CompetingRiskFunctions.class);
-        when(function4.calculateEventSpecificMortality(1, response4.getU())).thenReturn(mortalityOneArray[3]);
-        when(function4.calculateEventSpecificMortality(2, response4.getU())).thenReturn(mortalityTwoArray[3]);
-
-        final Forest mockForest = mock(Forest.class);
-        when(mockForest.evaluateOOB(dataset.get(0))).thenReturn(function1);
-        when(mockForest.evaluateOOB(dataset.get(1))).thenReturn(function2);
-        when(mockForest.evaluateOOB(dataset.get(2))).thenReturn(function3);
-        when(mockForest.evaluateOOB(dataset.get(3))).thenReturn(function4);
-
-
-        final CompetingRiskErrorRateCalculator errorRateCalculator = new CompetingRiskErrorRateCalculator(dataset, mockForest, true);
-
-        final double error = errorRateCalculator.calculateNaiveMortalityError(new int[]{1,2});
-
-        assertEquals(1.0/3.0, error);
-
-    }
-
-
 
 }