}

  static class MeanAndVariance {
    private final double mean;
    private final double variance;

    MeanAndVariance(double mean, double variance) {
      this.mean = mean;
      this.variance = variance;
    }

    @Override
    public int hashCode() {
      return Double.hashCode(mean) * 31 + Double.hashCode(variance);
    }
  }

  enum VarianceAlgorithm {
    DO_NOT_COMPUTE {
      @Override

  }

  static class MeanAndVariance {
    private final double mean;
    private final double variance;

    MeanAndVariance(double mean, double variance) {
      this.mean = mean;
      this.variance = variance;
    }

    @Override
    public int hashCode() {
      return Double.hashCode(mean) * 31 + Double.hashCode(variance);
    }
  }

  enum VarianceAlgorithm {
    DO_NOT_COMPUTE {
      @Override

   * either the {@code x} or {@code y} data must have a non-zero population variance (i.e. {@code
   * xStats().populationVariance() > 0.0 || yStats().populationVariance() > 0.0}). The result is
   * guaranteed to be horizontal if there is variance in the {@code x} data but not the {@code y}
   * data, and vertical if there is variance in the {@code y} data but not the {@code x} data.
   *

   * either the {@code x} or {@code y} data must have a non-zero population variance (i.e. {@code
   * xStats().populationVariance() > 0.0 || yStats().populationVariance() > 0.0}). The result is
   * guaranteed to be horizontal if there is variance in the {@code x} data but not the {@code y}
   * data, and vertical if there is variance in the {@code y} data but not the {@code x} data.
   *

    return Math.sqrt(populationVariance());
  }

  /**
   * Returns the <a href="http://en.wikipedia.org/wiki/Variance#Sample_variance">unbiased sample
   * variance</a> of the values. If this dataset is a sample drawn from a population, this is an
   * unbiased estimator of the population variance of the population. The count must be greater than
   * one.
   *

    return Math.sqrt(populationVariance());
  }

  /**
   * Returns the <a href="http://en.wikipedia.org/wiki/Variance#Sample_variance">unbiased sample
   * variance</a> of the values. If this dataset is a sample drawn from a population, this is an
   * unbiased estimator of the population variance of the population. The count must be greater than
   * one.
   *

                double variance = (maxTime - minTime) / avgTime;
                // JVM timing in concurrent scenarios is inherently variable
                // We verify implementation correctness rather than precise timing
                assertTrue(variance < 50.0,
                        String.format(

      if (values.hasAnyNonFinite()) {
        assertWithMessage("population variance of %s", values).that(populationVariance).isNaN();
        assertWithMessage("population variance by addAll(Stats) of %s", values)
            .that(populationVarianceByAddAllStats)
            .isNaN();
      } else {
        assertWithMessage("population variance of %s", values)
            .that(populationVariance)

### Do

* Ensure that the system executing your microbenchmarks has as little load as possible. Shutdown every process that can cause unnecessary
  runtime jitter. Watch the `Error` column in the benchmark results to see the run-to-run variance.
* Ensure to run enough warmup iterations to get the benchmark into a stable state. If you are unsure, don't change the defaults.
* Avoid CPU migrations by pinning your benchmarks to specific CPU cores. On Linux you can use `taskset`.

      if (values.hasAnyNonFinite()) {
        assertWithMessage("population variance of %s", values).that(populationVariance).isNaN();
        assertWithMessage("population variance by addAll(Stats) of %s", values)
            .that(populationVarianceByAddAllStats)
            .isNaN();
      } else {
        assertWithMessage("population variance of %s", values)
            .that(populationVariance)