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diff --git a/src/main/java/org/apache/commons/math3/stat/interval/IntervalUtils.java b/src/main/java/org/apache/commons/math3/stat/interval/IntervalUtils.java
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+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.commons.math3.stat.interval;
+
+import org.apache.commons.math3.exception.NotPositiveException;
+import org.apache.commons.math3.exception.NotStrictlyPositiveException;
+import org.apache.commons.math3.exception.NumberIsTooLargeException;
+import org.apache.commons.math3.exception.OutOfRangeException;
+import org.apache.commons.math3.exception.util.LocalizedFormats;
+
+/**
+ * Factory methods to generate confidence intervals for a binomial proportion.
+ * The supported methods are:
+ * <ul>
+ * <li>Agresti-Coull interval</li>
+ * <li>Clopper-Pearson method (exact method)</li>
+ * <li>Normal approximation (based on central limit theorem)</li>
+ * <li>Wilson score interval</li>
+ * </ul>
+ *
+ * @since 3.3
+ */
+public final class IntervalUtils {
+
+    /** Singleton Agresti-Coull instance. */
+    private static final BinomialConfidenceInterval AGRESTI_COULL = new AgrestiCoullInterval();
+
+    /** Singleton Clopper-Pearson instance. */
+    private static final BinomialConfidenceInterval CLOPPER_PEARSON = new ClopperPearsonInterval();
+
+    /** Singleton NormalApproximation instance. */
+    private static final BinomialConfidenceInterval NORMAL_APPROXIMATION = new NormalApproximationInterval();
+
+    /** Singleton Wilson score instance. */
+    private static final BinomialConfidenceInterval WILSON_SCORE = new WilsonScoreInterval();
+
+    /**
+     * Prevent instantiation.
+     */
+    private IntervalUtils() {
+    }
+
+    /**
+     * Create an Agresti-Coull binomial confidence interval for the true
+     * probability of success of an unknown binomial distribution with the given
+     * observed number of trials, successes and confidence level.
+     *
+     * @param numberOfTrials number of trials
+     * @param numberOfSuccesses number of successes
+     * @param confidenceLevel desired probability that the true probability of
+     *        success falls within the returned interval
+     * @return Confidence interval containing the probability of success with
+     *         probability {@code confidenceLevel}
+     * @throws NotStrictlyPositiveException if {@code numberOfTrials <= 0}.
+     * @throws NotPositiveException if {@code numberOfSuccesses < 0}.
+     * @throws NumberIsTooLargeException if {@code numberOfSuccesses > numberOfTrials}.
+     * @throws OutOfRangeException if {@code confidenceLevel} is not in the interval {@code (0, 1)}.
+     */
+    public static ConfidenceInterval getAgrestiCoullInterval(int numberOfTrials, int numberOfSuccesses,
+                                                             double confidenceLevel) {
+        return AGRESTI_COULL.createInterval(numberOfTrials, numberOfSuccesses, confidenceLevel);
+    }
+
+    /**
+     * Create a Clopper-Pearson binomial confidence interval for the true
+     * probability of success of an unknown binomial distribution with the given
+     * observed number of trials, successes and confidence level.
+     * <p>
+     * Preconditions:
+     * <ul>
+     * <li>{@code numberOfTrials} must be positive</li>
+     * <li>{@code numberOfSuccesses} may not exceed {@code numberOfTrials}</li>
+     * <li>{@code confidenceLevel} must be strictly between 0 and 1 (exclusive)</li>
+     * </ul>
+     * </p>
+     *
+     * @param numberOfTrials number of trials
+     * @param numberOfSuccesses number of successes
+     * @param confidenceLevel desired probability that the true probability of
+     *        success falls within the returned interval
+     * @return Confidence interval containing the probability of success with
+     *         probability {@code confidenceLevel}
+     * @throws NotStrictlyPositiveException if {@code numberOfTrials <= 0}.
+     * @throws NotPositiveException if {@code numberOfSuccesses < 0}.
+     * @throws NumberIsTooLargeException if {@code numberOfSuccesses > numberOfTrials}.
+     * @throws OutOfRangeException if {@code confidenceLevel} is not in the interval {@code (0, 1)}.
+     */
+    public static ConfidenceInterval getClopperPearsonInterval(int numberOfTrials, int numberOfSuccesses,
+                                                               double confidenceLevel) {
+        return CLOPPER_PEARSON.createInterval(numberOfTrials, numberOfSuccesses, confidenceLevel);
+    }
+
+    /**
+     * Create a binomial confidence interval for the true probability of success
+     * of an unknown binomial distribution with the given observed number of
+     * trials, successes and confidence level using the Normal approximation to
+     * the binomial distribution.
+     *
+     * @param numberOfTrials number of trials
+     * @param numberOfSuccesses number of successes
+     * @param confidenceLevel desired probability that the true probability of
+     *        success falls within the interval
+     * @return Confidence interval containing the probability of success with
+     *         probability {@code confidenceLevel}
+     */
+    public static ConfidenceInterval getNormalApproximationInterval(int numberOfTrials, int numberOfSuccesses,
+                                                                    double confidenceLevel) {
+        return NORMAL_APPROXIMATION.createInterval(numberOfTrials, numberOfSuccesses, confidenceLevel);
+    }
+
+    /**
+     * Create a Wilson score binomial confidence interval for the true
+     * probability of success of an unknown binomial distribution with the given
+     * observed number of trials, successes and confidence level.
+     *
+     * @param numberOfTrials number of trials
+     * @param numberOfSuccesses number of successes
+     * @param confidenceLevel desired probability that the true probability of
+     *        success falls within the returned interval
+     * @return Confidence interval containing the probability of success with
+     *         probability {@code confidenceLevel}
+     * @throws NotStrictlyPositiveException if {@code numberOfTrials <= 0}.
+     * @throws NotPositiveException if {@code numberOfSuccesses < 0}.
+     * @throws NumberIsTooLargeException if {@code numberOfSuccesses > numberOfTrials}.
+     * @throws OutOfRangeException if {@code confidenceLevel} is not in the interval {@code (0, 1)}.
+     */
+    public static ConfidenceInterval getWilsonScoreInterval(int numberOfTrials, int numberOfSuccesses,
+                                                            double confidenceLevel) {
+        return WILSON_SCORE.createInterval(numberOfTrials, numberOfSuccesses, confidenceLevel);
+    }
+
+    /**
+     * Verifies that parameters satisfy preconditions.
+     *
+     * @param numberOfTrials number of trials (must be positive)
+     * @param numberOfSuccesses number of successes (must not exceed numberOfTrials)
+     * @param confidenceLevel confidence level (must be strictly between 0 and 1)
+     * @throws NotStrictlyPositiveException if {@code numberOfTrials <= 0}.
+     * @throws NotPositiveException if {@code numberOfSuccesses < 0}.
+     * @throws NumberIsTooLargeException if {@code numberOfSuccesses > numberOfTrials}.
+     * @throws OutOfRangeException if {@code confidenceLevel} is not in the interval {@code (0, 1)}.
+     */
+    static void checkParameters(int numberOfTrials, int numberOfSuccesses, double confidenceLevel) {
+        if (numberOfTrials <= 0) {
+            throw new NotStrictlyPositiveException(LocalizedFormats.NUMBER_OF_TRIALS, numberOfTrials);
+        }
+        if (numberOfSuccesses < 0) {
+            throw new NotPositiveException(LocalizedFormats.NEGATIVE_NUMBER_OF_SUCCESSES, numberOfSuccesses);
+        }
+        if (numberOfSuccesses > numberOfTrials) {
+            throw new NumberIsTooLargeException(LocalizedFormats.NUMBER_OF_SUCCESS_LARGER_THAN_POPULATION_SIZE,
+                                                numberOfSuccesses, numberOfTrials, true);
+        }
+        if (confidenceLevel <= 0 || confidenceLevel >= 1) {
+            throw new OutOfRangeException(LocalizedFormats.OUT_OF_BOUNDS_CONFIDENCE_LEVEL,
+                                          confidenceLevel, 0, 1);
+        }
+    }
+
+}