diff --git a/java/unittests/infodynamics/measures/continuous/gaussian/MutualInfoMultiVariateTester.java b/java/unittests/infodynamics/measures/continuous/gaussian/MutualInfoMultiVariateTester.java
new file mode 100755
index 0000000..86dd1e8
--- /dev/null
+++ b/java/unittests/infodynamics/measures/continuous/gaussian/MutualInfoMultiVariateTester.java
@@ -0,0 +1,50 @@
+package infodynamics.measures.continuous.gaussian;
+
+import junit.framework.TestCase;
+import infodynamics.utils.EmpiricalMeasurementDistribution;
+import infodynamics.utils.RandomGenerator;
+
+public class MutualInfoMultiVariateTester extends TestCase {
+
+	public void testComputeSignificanceDoesntAlterAverage() throws Exception {
+		
+		MutualInfoCalculatorMultiVariateGaussian miCalc =
+				new MutualInfoCalculatorMultiVariateGaussian();
+		
+		int dimensions = 2;
+		int timeSteps = 100;
+		
+		miCalc.initialise(dimensions, dimensions);
+		
+		// generate some random data
+		RandomGenerator rg = new RandomGenerator();
+		double[][] sourceData = rg.generateNormalData(timeSteps, dimensions,
+				0, 1);
+		double[][] destData = rg.generateNormalData(timeSteps, dimensions,
+				0, 1);
+		
+		miCalc.setObservations(sourceData, destData);
+		
+		//miCalc.setDebug(true);
+		double mi = miCalc.computeAverageLocalOfObservations();
+		//miCalc.setDebug(false);
+		//double[] miLocal = miCalc.computeLocalOfPreviousObservations();
+		
+		System.out.printf("Average was %.5f\n", mi);
+		
+		// Now look at statistical significance tests
+		int[][] newOrderings = rg.generateDistinctRandomPerturbations(
+				timeSteps, 100);
+
+		EmpiricalMeasurementDistribution measDist =
+				miCalc.computeSignificance(newOrderings);
+		
+		System.out.printf("pValue of sig test was %.3f\n", measDist.pValue);
+		
+		// And compute the average value again to check that it's consistent:
+		for (int i = 0; i < 10; i++) {
+			double averageCheck1 = miCalc.computeAverageLocalOfObservations();
+			assertEquals(mi, averageCheck1);
+		}
+	}
+}
diff --git a/java/unittests/infodynamics/measures/continuous/kernel/MutualInfoMultiVariateTester.java b/java/unittests/infodynamics/measures/continuous/kernel/MutualInfoMultiVariateTester.java
new file mode 100755
index 0000000..cced3c8
--- /dev/null
+++ b/java/unittests/infodynamics/measures/continuous/kernel/MutualInfoMultiVariateTester.java
@@ -0,0 +1,50 @@
+package infodynamics.measures.continuous.kernel;
+
+import junit.framework.TestCase;
+import infodynamics.utils.RandomGenerator;
+
+public class MutualInfoMultiVariateTester extends TestCase {
+
+	public void testComputeSignificanceDoesntAlterAverage() throws Exception {
+		
+		MutualInfoCalculatorMultiVariateKernel miCalc =
+				new MutualInfoCalculatorMultiVariateKernel();
+		
+		int dimensions = 2;
+		int timeSteps = 100;
+		double kernelWidth = 1;
+		
+		miCalc.setProperty(
+				MutualInfoCalculatorMultiVariateKernel.NORMALISE_PROP_NAME,
+				"true");
+		miCalc.initialise(dimensions, dimensions, kernelWidth);
+		
+		// generate some random data
+		RandomGenerator rg = new RandomGenerator();
+		double[][] sourceData = rg.generateNormalData(timeSteps, dimensions,
+				0, 1);
+		double[][] destData = rg.generateNormalData(timeSteps, dimensions,
+				0, 1);
+		
+		miCalc.setObservations(sourceData, destData);
+		
+		//miCalc.setDebug(true);
+		double mi = miCalc.computeAverageLocalOfObservations();
+		//miCalc.setDebug(false);
+		//double[] miLocal = miCalc.computeLocalOfPreviousObservations();
+		
+		System.out.printf("Average was %.5f\n", mi);
+		
+		// Now look at statistical significance tests
+		int[][] newOrderings = rg.generateDistinctRandomPerturbations(
+				timeSteps, 100);
+
+		miCalc.computeSignificance(newOrderings);
+		
+		// And compute the average value again to check that it's consistent:
+		for (int i = 0; i < 10; i++) {
+			double averageCheck1 = miCalc.computeAverageLocalOfObservations();
+			assertEquals(mi, averageCheck1);
+		}
+	}
+}