public double getStandardError() {
        Set<V> overlap = new HashSet<V>(baselineMetricPerDimension.keySet());
        overlap.retainAll(testMetricPerDimension.keySet());

        // paired or matched samples --> analyse distribution of difference scores
        SummaryStatistics differences = new SummaryStatistics();
        for (V key : overlap) {
            double diff = baselineMetricPerDimension.get(key) - testMetricPerDimension.get(key);
            differences.addValue(diff);
        }

        double e = differences.getStandardDeviation() / Math.sqrt(differences.getN());
        return e;
    }
public double getStandardError ( ) { Set < V > overlap = new HashSet < V > ( baselineMetricPerDimension . keySet ( ) ) ; overlap . retainAll ( testMetricPerDimension . keySet ( ) ) ; SummaryStatistics differences = new SummaryStatistics ( ) ; for ( V key : overlap ) { double diff = baselineMetricPerDimension . get ( key ) - testMetricPerDimension . get ( key ) ; differences . addValue ( diff ) ; } double e = differences . getStandardDeviation ( ) / Math . sqrt ( differences . getN ( ) ) ; return e ; }
Implements equation (8.13) from "Elementary Statistics: A Problem Solving
Approach 4th Edition", Andrew L. Comrey, Howard B. Lee

@return the standard error as the ratio of the standard deviation divided
by the sqrt(number of users) of the distribution of difference scores.