* 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.
   *

        ApiTypeUsage(
            sourceName,
            isNullable,
            type(sourceName),
            variance,
            typeArguments.map { apiTypeUsageFor(it.binaryName, variance = it.variance, typeArguments = it.typeArguments) },
            bounds.map { apiTypeUsageFor(it.binaryName, variance = it.variance, typeArguments = it.typeArguments) }
        )
    }


internal
val ApiTypeUsage.isStarProjectionTypeUsage

   * 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.
   *

                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(

private
fun ApiTypeUsage.toTypeArgumentString(): String =
    "${variance.toKotlinString()}$sourceName${typeArguments.toTypeArgumentsString(type)}${isNullable.toKotlinNullabilityString()}"


private
fun Variance.toKotlinString() =
    when (this) {
        Variance.INVARIANT -> ""
        Variance.COVARIANT -> "out "
        Variance.CONTRAVARIANT -> "in "
    }


private

    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.
   *

      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)

    boolean longWait = NANOSECONDS.toSeconds(thread.timeSpentBlocked) >= 5;
    // Count how long it actually took to return; we'll accept any number between the expected delay
    // and the approximate actual delay, to be robust to variance in thread scheduling.
    char overWaitNanosFirstDigit =
        Long.toString(thread.timeSpentBlocked - MILLISECONDS.toNanos(longWait ? 5000 : 3000))
            .charAt(0);
    if (overWaitNanosFirstDigit < '4') {

    boolean longWait = NANOSECONDS.toSeconds(thread.timeSpentBlocked) >= 5;
    // Count how long it actually took to return; we'll accept any number between the expected delay
    // and the approximate actual delay, to be robust to variance in thread scheduling.
    char overWaitNanosFirstDigit =
        Long.toString(thread.timeSpentBlocked - MILLISECONDS.toNanos(longWait ? 5000 : 3000))
            .charAt(0);
    if (overWaitNanosFirstDigit < '4') {