A couple of questions

Is a high variance good or bad?

Variance is neither good nor bad for investors in and of itself. However, high variance in a stock is associated with higher risk, along with a higher return. … Risk reflects the chance that an investment's actual return, or its gain or loss over a specific period, is higher or lower than expected.

What does it mean when the variance is high?

A large variance indicates that numbers in the set are far from the mean and far from each other. A small variance, on the other hand, indicates the opposite. A variance value of zero, though, indicates that all values within a set of numbers are identical. Every variance that isn't zero is a positive number.

Is low or high variability better?

Low variability is ideal because it means that you can better predict information about the population based on sample data. High variability means that the values are less consistent, so it's harder to make predictions.

Is high variance good or bad psychology?

Variance is always non-negative, a small variance indicates that the data points tend to be very close to the mean (expected value) and hence to each other, while a high variance indicates that the data points are very spread out around the mean and from each other.

What is good and bad variance?

Good implied variance is identified by call options that pay off only in case the return realization is positive, and bad implied variance is characterized by put options that pay off only if a negative return is realized.

Why is high variability bad?

Higher variability reduces your ability to detect statistical significance. … However, for statistical analysis, we almost always use samples from the population, which provides a fuzzier picture. For random samples, increasing the sample size is like increasing the resolution of a picture of the populations.

Is high variance good or bad in machine learning?

If a learning algorithm is suffering from high variance, getting more training data helps a lot. High variance and low bias means overfitting. This is caused by understanding the data to well. With more data, it will find the signal and not the noise.

What is considered low-variance?

Distributions with a coefficient of variation to be less than 1 are considered to be low-variance, whereas those with a CV higher than 1 are considered to be high variance.