assertThrows(IllegalStateException.class, () -> emptyAccumulator.populationStandardDeviation());
    assertThrows(
        IllegalStateException.class,
        () -> emptyAccumulatorByAddAllEmptyIterable.populationStandardDeviation());
    assertThrows(
        IllegalStateException.class,
        () -> emptyAccumulatorByAddAllEmptyStats.populationStandardDeviation());

                / (twoValuesAccumulator.xStats().populationStandardDeviation()
                    * twoValuesAccumulator.yStats().populationStandardDeviation()));
    assertThat(manyValuesAccumulator.pearsonsCorrelationCoefficient())
        .isWithin(ALLOWED_ERROR)
        .of(
            manyValuesAccumulator.populationCovariance()
                / (manyValuesAccumulator.xStats().populationStandardDeviation()

    assertThrows(
        IllegalStateException.class, () -> EMPTY_STATS_VARARGS.populationStandardDeviation());
    assertThrows(
        IllegalStateException.class, () -> EMPTY_STATS_ITERABLE.populationStandardDeviation());
    assertThat(ONE_VALUE_STATS.populationStandardDeviation()).isEqualTo(0.0);
    assertThat(TWO_VALUES_STATS.populationStandardDeviation())
        .isWithin(ALLOWED_ERROR)

    assertThrows(IllegalStateException.class, () -> emptyAccumulator.populationStandardDeviation());
    assertThrows(
        IllegalStateException.class,
        () -> emptyAccumulatorByAddAllEmptyIterable.populationStandardDeviation());
    assertThrows(
        IllegalStateException.class,
        () -> emptyAccumulatorByAddAllEmptyStats.populationStandardDeviation());

                / (twoValuesAccumulator.xStats().populationStandardDeviation()
                    * twoValuesAccumulator.yStats().populationStandardDeviation()));
    assertThat(manyValuesAccumulator.pearsonsCorrelationCoefficient())
        .isWithin(ALLOWED_ERROR)
        .of(
            manyValuesAccumulator.populationCovariance()
                / (manyValuesAccumulator.xStats().populationStandardDeviation()

        .isWithin(ALLOWED_ERROR)
        .of(
            TWO_VALUES_PAIRED_STATS.populationCovariance()
                / (TWO_VALUES_PAIRED_STATS.xStats().populationStandardDeviation()
                    * TWO_VALUES_PAIRED_STATS.yStats().populationStandardDeviation()));
    // For datasets of many double values, we test many combinations of finite and non-finite
    // y-values:
    for (ManyValues values : ALL_MANY_VALUES) {

  @Override
  public String toString() {
    if (count() > 0) {
      return MoreObjects.toStringHelper(this)
          .add("count", count)
          .add("mean", mean)
          .add("populationStandardDeviation", populationStandardDeviation())
          .add("min", min)
          .add("max", max)
          .toString();
    } else {
      return MoreObjects.toStringHelper(this).add("count", count).toString();
    }
  }

   * Double#NEGATIVE_INFINITY}, or {@link Double#NaN}) then the result is {@link Double#NaN}.
   *
   * @throws IllegalStateException if the dataset is empty
   */
  public final double populationStandardDeviation() {
    return Math.sqrt(populationVariance());
  }

  /**
   * Returns the <a href="http://en.wikipedia.org/wiki/Variance#Sample_variance">unbiased sample