Refactors

- getNode method to handle invalid node index and improve error handling
- update parameter description in SimulationMultiple constructor
- enhance calcError method in StatisticsSummary for better clarity and functionality.

src/main/java/net/berack/upo/valpre/sim/Net.java

@@ -127,7 +127,8 @@ public final class Net implements Iterable<ServerNode> {
      * @return the node
      */
     public ServerNode getNode(String name) {
-        return this.servers.get(this.getNodeIndex(name));
+        var index = this.getNodeIndex(name);
+        return index < 0 ? null : this.servers.get(index);
     }
 
     /**

src/main/java/net/berack/upo/valpre/sim/SimulationMultiple.java

@@ -18,7 +18,7 @@ public class SimulationMultiple {
     /**
      * Create a new object that can simulate the net in input multiple times
      * 
-     * @param net the net that sould be simulated
+     * @param net the net that should be simulated
      */
     public SimulationMultiple(Net net) {
         this.net = net;
@@ -85,5 +85,4 @@ public class SimulationMultiple {
             return new ResultSummary(results);
         }
     }
-
 }

src/main/java/net/berack/upo/valpre/sim/stats/StatisticsSummary.java

@@ -45,21 +45,6 @@ public class StatisticsSummary {
         this.distr = new TDistribution(null, values.length - 1);
     }
 
-    /**
-     * Calculates the error at the selected alpha level.
-     * This method computes the error for the average and standard deviation values,
-     * considering the sample size and the confidence level (alpha).
-     * The result is adjusted using a t-distribution to account for the variability
-     * in smaller sample sizes.
-     * 
-     * @param alpha the alpha value
-     * @return the error of the values
-     */
-    public double calcError(double alpha) {
-        var percentile = this.distr.inverseCumulativeProbability(alpha);
-        return percentile * (this.stdDev / Math.sqrt(this.values.length));
-    }
-
     /**
      * Get the frequency of the values in the array.
      * 
@@ -90,6 +75,38 @@ public class StatisticsSummary {
         return this.values[index];
     }
 
+    /**
+     * Calculates the error at the selected alpha level.
+     * This method computes the error for the average and standard deviation values,
+     * considering the sample size and the confidence level (alpha).
+     * The result is adjusted using a t-distribution to account for the variability
+     * in smaller sample sizes.
+     * 
+     * @param alpha the alpha value
+     * @return the error of the values
+     */
+    public double calcError(double alpha) {
+        return StatisticsSummary.calcError(this.distr, this.stdDev, alpha);
+    }
+
+    /**
+     * Calculates the error at the selected alpha level.
+     * This method computes the error for the average and standard deviation values,
+     * considering the sample size and the confidence level (alpha).
+     * The result is adjusted using a t-distribution to account for the variability
+     * in smaller sample sizes.
+     * 
+     * @param distribution the t-distribution to use
+     * @param stdDev       the standard deviation of the values
+     * @param alpha        the alpha value
+     * @return the error of the values
+     */
+    public static double calcError(TDistribution distribution, double stdDev, double alpha) {
+        var percentile = distribution.inverseCumulativeProbability(alpha);
+        var n = distribution.getDegreesOfFreedom() + 1;
+        return percentile * (stdDev / Math.sqrt(n));
+    }
+
     /**
      * Get a summary of the statistics.
      *