assertThat(ONE_VALUE_PAIRED_STATS.populationCovariance()).isEqualTo(0.0);
    assertThat(createSingleStats(Double.POSITIVE_INFINITY, 1.23).populationCovariance()).isNaN();
    assertThat(createSingleStats(Double.NEGATIVE_INFINITY, 1.23).populationCovariance()).isNaN();
    assertThat(createSingleStats(Double.NaN, 1.23).populationCovariance()).isNaN();

    assertThat(ONE_VALUE_PAIRED_STATS.populationCovariance()).isEqualTo(0.0);
    assertThat(createSingleStats(Double.POSITIVE_INFINITY, 1.23).populationCovariance()).isNaN();
    assertThat(createSingleStats(Double.NEGATIVE_INFINITY, 1.23).populationCovariance()).isNaN();
    assertThat(createSingleStats(Double.NaN, 1.23).populationCovariance()).isNaN();

    assertThrows(IllegalStateException.class, () -> emptyAccumulator.populationCovariance());
    assertThrows(
        IllegalStateException.class,
        () -> emptyAccumulatorByAddAllEmptyPairedStats.populationCovariance());
    assertThat(oneValueAccumulator.populationCovariance()).isEqualTo(0.0);
    assertThat(oneValueAccumulatorByAddAllEmptyPairedStats.populationCovariance()).isEqualTo(0.0);

  @Override
  public String toString() {
    if (count() > 0) {
      return MoreObjects.toStringHelper(this)
          .add("xStats", xStats)
          .add("yStats", yStats)
          .add("populationCovariance", populationCovariance())
          .toString();
    } else {
      return MoreObjects.toStringHelper(this)
          .add("xStats", xStats)
          .add("yStats", yStats)
          .toString();
    }
  }

   * product-moment correlation coefficient</a> of the values. The count must greater than one, and
   * the {@code x} and {@code y} values must both have non-zero population variance (i.e. {@code
   * xStats().populationVariance() > 0.0 && yStats().populationVariance() > 0.0}). The result is not
   * guaranteed to be exactly +/-1 even when the data are perfectly (anti-)correlated, due to
   * numerical errors. However, it is guaranteed to be in the inclusive range [-1, +1].

    assertThrows(IllegalStateException.class, EMPTY_STATS_ITERABLE::populationVariance);
    assertThat(ONE_VALUE_STATS.populationVariance()).isEqualTo(0.0);
    assertThat(Stats.of(POSITIVE_INFINITY).populationVariance()).isNaN();
    assertThat(Stats.of(NEGATIVE_INFINITY).populationVariance()).isNaN();
    assertThat(Stats.of(NaN).populationVariance()).isNaN();

    assertThrows(IllegalStateException.class, () -> emptyAccumulator.populationVariance());
    assertThrows(
        IllegalStateException.class,
        () -> emptyAccumulatorByAddAllEmptyIterable.populationVariance());
    assertThrows(
        IllegalStateException.class, () -> emptyAccumulatorByAddAllEmptyStats.populationVariance());
    assertThat(oneValueAccumulator.populationVariance()).isEqualTo(0.0);

    assertThrows(IllegalStateException.class, () -> emptyAccumulator.populationVariance());
    assertThrows(
        IllegalStateException.class,
        () -> emptyAccumulatorByAddAllEmptyIterable.populationVariance());
    assertThrows(
        IllegalStateException.class, () -> emptyAccumulatorByAddAllEmptyStats.populationVariance());
    assertThat(oneValueAccumulator.populationVariance()).isEqualTo(0.0);

   */
  public double sum() {
    return mean * count;
  }

  /**
   * Returns the <a href="http://en.wikipedia.org/wiki/Variance#Population_variance">population
   * variance</a> of the values. The count must be non-zero.
   *
   * <p>This is guaranteed to return zero if the dataset contains only exactly one finite value. It

      assertThat(actualStats.populationVariance()).isEqualTo(0.0);
      assertThat(actualStats.min()).isWithin(ALLOWED_ERROR).of(expectedStats.min());
      assertThat(actualStats.max()).isWithin(ALLOWED_ERROR).of(expectedStats.max());
    } else {
      assertThat(actualStats.mean()).isWithin(ALLOWED_ERROR).of(expectedStats.mean());
      assertThat(actualStats.populationVariance())
          .isWithin(ALLOWED_ERROR)