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diff --git a/core/src/main/scala/org/apache/spark/partial/SumEvaluator.scala b/core/src/main/scala/org/apache/spark/partial/SumEvaluator.scala
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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.spark.partial
+
+import cern.jet.stat.Probability
+
+import org.apache.spark.util.StatCounter
+
+/**
+ * An ApproximateEvaluator for sums. It estimates the mean and the cont and multiplies them
+ * together, then uses the formula for the variance of two independent random variables to get
+ * a variance for the result and compute a confidence interval.
+ */
+private[spark] class SumEvaluator(totalOutputs: Int, confidence: Double)
+  extends ApproximateEvaluator[StatCounter, BoundedDouble] {
+
+  var outputsMerged = 0
+  var counter = new StatCounter
+
+  override def merge(outputId: Int, taskResult: StatCounter) {
+    outputsMerged += 1
+    counter.merge(taskResult)
+  }
+
+  override def currentResult(): BoundedDouble = {
+    if (outputsMerged == totalOutputs) {
+      new BoundedDouble(counter.sum, 1.0, counter.sum, counter.sum)
+    } else if (outputsMerged == 0) {
+      new BoundedDouble(0, 0.0, Double.NegativeInfinity, Double.PositiveInfinity)
+    } else {
+      val p = outputsMerged.toDouble / totalOutputs
+      val meanEstimate = counter.mean
+      val meanVar = counter.sampleVariance / counter.count
+      val countEstimate = (counter.count + 1 - p) / p
+      val countVar = (counter.count + 1) * (1 - p) / (p * p)
+      val sumEstimate = meanEstimate * countEstimate
+      val sumVar = (meanEstimate * meanEstimate * countVar) +
+                   (countEstimate * countEstimate * meanVar) +
+                   (meanVar * countVar)
+      val sumStdev = math.sqrt(sumVar)
+      val confFactor = {
+        if (counter.count > 100) {
+          Probability.normalInverse(1 - (1 - confidence) / 2)
+        } else {
+          Probability.studentTInverse(1 - confidence, (counter.count - 1).toInt)
+        }
+      }
+      val low = sumEstimate - confFactor * sumStdev
+      val high = sumEstimate + confFactor * sumStdev
+      new BoundedDouble(sumEstimate, confidence, low, high)
+    }
+  }
+}