diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/BinomialTest.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/BinomialTest.java
index a7deb3187..9a601bb38 100644
--- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/BinomialTest.java
+++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/BinomialTest.java
@@ -122,7 +122,7 @@ public class BinomialTest {
         final BinomialDistribution distribution = BinomialDistribution.of(numberOfTrials, probability);
         switch (alternativeHypothesis) {
         case GREATER_THAN:
-            return 1 - distribution.cumulativeProbability(numberOfSuccesses - 1);
+            return distribution.survivalProbability(numberOfSuccesses - 1);
         case LESS_THAN:
             return distribution.cumulativeProbability(numberOfSuccesses);
         case TWO_SIDED:
diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/ChiSquareTest.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/ChiSquareTest.java
index eef09933f..3cf36875d 100644
--- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/ChiSquareTest.java
+++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/ChiSquareTest.java
@@ -157,7 +157,7 @@ public class ChiSquareTest {
         // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
         final ChiSquaredDistribution distribution =
             ChiSquaredDistribution.of(expected.length - 1.0);
-        return 1.0 - distribution.cumulativeProbability(chiSquare(expected, observed));
+        return distribution.survivalProbability(chiSquare(expected, observed));
     }
 
     /**
@@ -330,7 +330,7 @@ public class ChiSquareTest {
         double df = ((double) counts.length -1) * ((double) counts[0].length - 1);
         // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
         final ChiSquaredDistribution distribution = ChiSquaredDistribution.of(df);
-        return 1 - distribution.cumulativeProbability(chiSquare(counts));
+        return distribution.survivalProbability(chiSquare(counts));
     }
 
     /**
@@ -532,7 +532,7 @@ public class ChiSquareTest {
         // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
         final ChiSquaredDistribution distribution =
                 ChiSquaredDistribution.of((double) observed1.length - 1);
-        return 1 - distribution.cumulativeProbability(
+        return distribution.survivalProbability(
                 chiSquareDataSetsComparison(observed1, observed2));
     }
 
diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/GTest.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/GTest.java
index 42273e625..3b82b74a7 100644
--- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/GTest.java
+++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/GTest.java
@@ -155,7 +155,7 @@ public class GTest {
         // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
         final ChiSquaredDistribution distribution =
                 ChiSquaredDistribution.of(expected.length - 1.0);
-        return 1.0 - distribution.cumulativeProbability(g(expected, observed));
+        return distribution.survivalProbability(g(expected, observed));
     }
 
     /**
@@ -186,7 +186,7 @@ public class GTest {
         // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
         final ChiSquaredDistribution distribution =
                 ChiSquaredDistribution.of(expected.length - 2.0);
-        return 1.0 - distribution.cumulativeProbability(g(expected, observed));
+        return distribution.survivalProbability(g(expected, observed));
     }
 
     /**
@@ -476,7 +476,7 @@ public class GTest {
         // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
         final ChiSquaredDistribution distribution =
                 ChiSquaredDistribution.of((double) observed1.length - 1);
-        return 1 - distribution.cumulativeProbability(
+        return distribution.survivalProbability(
                 gDataSetsComparison(observed1, observed2));
     }
 
diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/OneWayAnova.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/OneWayAnova.java
index 2643270e0..8b93f1775 100644
--- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/OneWayAnova.java
+++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/OneWayAnova.java
@@ -104,9 +104,9 @@ public class OneWayAnova {
      * {@link org.apache.commons.statistics.distribution.FDistribution
      * commons-math F Distribution implementation} to estimate the exact
      * p-value, using the formula<pre>
-     *   p = 1 - cumulativeProbability(F)</pre>
-     * where <code>F</code> is the F value and <code>cumulativeProbability</code>
-     * is the commons-math implementation of the F distribution.
+     *   p = survivalProbability(F)</pre>
+     * where <code>F</code> is the F value and <code>survivalProbability = 1 - cumulativeProbability</code>
+     * is the commons-statistics implementation of the F distribution.
      *
      * @param categoryData <code>Collection</code> of <code>double[]</code>
      * arrays each containing data for one category
@@ -126,7 +126,7 @@ public class OneWayAnova {
         // No try-catch or advertised exception because args are valid
         // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
         final FDistribution fdist = FDistribution.of(a.dfbg, a.dfwg);
-        return 1.0 - fdist.cumulativeProbability(a.f);
+        return fdist.survivalProbability(a.f);
     }
 
     /**
@@ -142,9 +142,9 @@ public class OneWayAnova {
      * {@link org.apache.commons.statistics.distribution.FDistribution
      * commons-math F Distribution implementation} to estimate the exact
      * p-value, using the formula<pre>
-     *   p = 1 - cumulativeProbability(F)</pre>
-     * where <code>F</code> is the F value and <code>cumulativeProbability</code>
-     * is the commons-math implementation of the F distribution.
+     *   p = survivalProbability(F)</pre>
+     * where <code>F</code> is the F value and <code>survivalProbability = 1 - cumulativeProbability</code>
+     * is the commons-statistics implementation of the F distribution.
      *
      * @param categoryData <code>Collection</code> of {@link SummaryStatistics}
      * each containing data for one category
@@ -167,7 +167,7 @@ public class OneWayAnova {
         final AnovaStats a = anovaStats(categoryData, allowOneElementData);
         // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
         final FDistribution fdist = FDistribution.of(a.dfbg, a.dfwg);
-        return 1.0 - fdist.cumulativeProbability(a.f);
+        return fdist.survivalProbability(a.f);
     }
 
     /**
@@ -221,9 +221,9 @@ public class OneWayAnova {
      * {@link org.apache.commons.statistics.distribution.FDistribution
      * commons-math F Distribution implementation} to estimate the exact
      * p-value, using the formula<pre>
-     *   p = 1 - cumulativeProbability(F)</pre>
-     * where <code>F</code> is the F value and <code>cumulativeProbability</code>
-     * is the commons-math implementation of the F distribution.
+     *   p = survivalProbability(F)</pre>
+     * where <code>F</code> is the F value and <code>survivalProbability = 1 - cumulativeProbability</code>
+     * is the commons-statistics implementation of the F distribution.
      * <p>True is returned iff the estimated p-value is less than alpha.</p>
      *
      * @param categoryData <code>Collection</code> of <code>double[]</code>
diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/AgrestiCoullInterval.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/AgrestiCoullInterval.java
index 1002abb7e..95d4caea6 100644
--- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/AgrestiCoullInterval.java
+++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/AgrestiCoullInterval.java
@@ -35,7 +35,7 @@ public class AgrestiCoullInterval implements BinomialConfidenceInterval {
         IntervalUtils.checkParameters(numberOfTrials, numberOfSuccesses, confidenceLevel);
         final double alpha = (1.0 - confidenceLevel) / 2;
         final NormalDistribution normalDistribution = NormalDistribution.of(0, 1);
-        final double z = normalDistribution.inverseCumulativeProbability(1 - alpha);
+        final double z = normalDistribution.inverseSurvivalProbability(alpha);
         final double zSquared = JdkMath.pow(z, 2);
         final double modifiedNumberOfTrials = numberOfTrials + zSquared;
         final double modifiedSuccessesRatio = (1.0 / modifiedNumberOfTrials) * (numberOfSuccesses + 0.5 * zSquared);
diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/ClopperPearsonInterval.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/ClopperPearsonInterval.java
index a28522cdc..b8b23034e 100644
--- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/ClopperPearsonInterval.java
+++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/ClopperPearsonInterval.java
@@ -42,7 +42,7 @@ public class ClopperPearsonInterval implements BinomialConfidenceInterval {
         if (numberOfSuccesses > 0) {
             final FDistribution distributionLowerBound = FDistribution.of(2.0 * (numberOfTrials - numberOfSuccesses + 1),
                                                                           2.0 * numberOfSuccesses);
-            final double fValueLowerBound = distributionLowerBound.inverseCumulativeProbability(1 - alpha);
+            final double fValueLowerBound = distributionLowerBound.inverseSurvivalProbability(alpha);
             lowerBound = numberOfSuccesses /
                 (numberOfSuccesses + (numberOfTrials - numberOfSuccesses + 1) * fValueLowerBound);
         }
@@ -50,7 +50,7 @@ public class ClopperPearsonInterval implements BinomialConfidenceInterval {
         if (numberOfSuccesses < numberOfTrials) {
             final FDistribution distributionUpperBound = FDistribution.of(2.0 * (numberOfSuccesses + 1),
                                                                           2.0 * (numberOfTrials - numberOfSuccesses));
-            final double fValueUpperBound = distributionUpperBound.inverseCumulativeProbability(1 - alpha);
+            final double fValueUpperBound = distributionUpperBound.inverseSurvivalProbability(alpha);
             upperBound = (numberOfSuccesses + 1) * fValueUpperBound /
                 (numberOfTrials - numberOfSuccesses + (numberOfSuccesses + 1) * fValueUpperBound);
         }
diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/NormalApproximationInterval.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/NormalApproximationInterval.java
index fb085432d..8871fcaa3 100644
--- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/NormalApproximationInterval.java
+++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/NormalApproximationInterval.java
@@ -37,7 +37,7 @@ public class NormalApproximationInterval implements BinomialConfidenceInterval {
         final double mean = (double) numberOfSuccesses / (double) numberOfTrials;
         final double alpha = (1.0 - confidenceLevel) / 2;
         final NormalDistribution normalDistribution = NormalDistribution.of(0, 1);
-        final double difference = normalDistribution.inverseCumulativeProbability(1 - alpha) *
+        final double difference = normalDistribution.inverseSurvivalProbability(alpha) *
                                   JdkMath.sqrt(1.0 / numberOfTrials * mean * (1 - mean));
         return new ConfidenceInterval(mean - difference, mean + difference, confidenceLevel);
     }
diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/WilsonScoreInterval.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/WilsonScoreInterval.java
index e08d58714..c57c122df 100644
--- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/WilsonScoreInterval.java
+++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/WilsonScoreInterval.java
@@ -33,7 +33,7 @@ public class WilsonScoreInterval implements BinomialConfidenceInterval {
         IntervalUtils.checkParameters(numberOfTrials, numberOfSuccesses, confidenceLevel);
         final double alpha = (1 - confidenceLevel) / 2;
         final NormalDistribution normalDistribution = NormalDistribution.of(0, 1);
-        final double z = normalDistribution.inverseCumulativeProbability(1 - alpha);
+        final double z = normalDistribution.inverseSurvivalProbability(alpha);
         final double zSquared = z * z;
         final double oneOverNumTrials = 1d / numberOfTrials;
         final double zSquaredOverNumTrials = zSquared * oneOverNumTrials;
diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/regression/SimpleRegression.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/regression/SimpleRegression.java
index ada0c3b2a..44d73219c 100644
--- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/regression/SimpleRegression.java
+++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/regression/SimpleRegression.java
@@ -695,7 +695,7 @@ public class SimpleRegression implements UpdatingMultipleLinearRegression {
         // No advertised NotStrictlyPositiveException here - will return NaN above
         TDistribution distribution = TDistribution.of(n - 2d);
         return getSlopeStdErr() *
-            distribution.inverseCumulativeProbability(1d - alpha / 2d);
+            distribution.inverseSurvivalProbability(alpha / 2d);
     }
 
     /**
@@ -726,7 +726,7 @@ public class SimpleRegression implements UpdatingMultipleLinearRegression {
         }
         // No advertised NotStrictlyPositiveException here - will return NaN above
         TDistribution distribution = TDistribution.of(n - 2d);
-        return 2d * (1.0 - distribution.cumulativeProbability(
+        return 2d * (distribution.survivalProbability(
                     JdkMath.abs(getSlope()) / getSlopeStdErr()));
     }
 
diff --git a/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/stat/correlation/PearsonsCorrelationTest.java b/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/stat/correlation/PearsonsCorrelationTest.java
index 87f5631f9..cb1d36a6b 100644
--- a/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/stat/correlation/PearsonsCorrelationTest.java
+++ b/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/stat/correlation/PearsonsCorrelationTest.java
@@ -237,7 +237,7 @@ public class PearsonsCorrelationTest {
         for (int i = 0; i < 5; i++) {
             for (int j = 0; j < i; j++) {
                 double t = JdkMath.abs(rValues.getEntry(i, j)) / stdErrors.getEntry(i, j);
-                double p = 2 * (1 - tDistribution.cumulativeProbability(t));
+                double p = 2 * tDistribution.survivalProbability(t);
                 Assert.assertEquals(p, pValues.getEntry(i, j), 10E-15);
             }
         }