Variance

By:

Arti

Arti is a specialized AI Financial Assistant at Invezz, created to support the editorial team. He leverages both AI and the Invezz.com knowledge base, understands over 100,000... read more.

Reviewed by:

Prash Raval

Prash is a financial writer for Invezz covering FX, the stock market and investing. For over a decade he has traded spot FX full time while... read more.

Updated: May 29, 2024

Ad disclosure

Invezz is an independent platform with the goal of helping users achieve financial freedom. In order to fund our work, we partner with advertisers who may pay to be displayed in certain positions on certain pages, or may compensate us for referring users to their services. While our reviews and assessments of each product are independent and unbiased, the order in which brands are presented and the placement of offers may be impacted and some of the links on this page may be affiliate links from which we earn a commission. The order in which products and services appear on Invezz does not represent an endorsement from us, and please be aware that there may be other platforms available to you than the products and services that appear on our website. Read more about how we make money >

3 key takeaways

• Variance quantifies the degree of spread in a data set, showing how far each value in the set is from the mean.
• A high variance indicates that the values are spread out over a wide range, while a low variance indicates that the values are close to the mean.
• Variance is an essential concept in statistics, finance, and other fields for analyzing variability, risk, and performance.

What is variance?

Variance is a measure of how much the values in a data set differ from the mean (average) of the set. It is calculated by taking the average of the squared differences between each value and the mean. Variance provides insights into the variability or volatility of data, helping to understand the distribution and predictability of the data set.

How is variance calculated?

Variance (σ² for a population and s² for a sample) is calculated using the following steps:

1. Calculate the mean (average): Sum all the values in the data set and divide by the number of values.
2. Subtract the mean from each value: This gives the deviation of each value from the mean.
3. Square each deviation: This removes negative values and emphasizes larger deviations.
4. Calculate the average of the squared deviations: For a population, sum all the squared deviations and divide by the number of values. For a sample, divide by the number of values minus one (n – 1) to account for sample bias.

Importance of variance

Variance is crucial for several reasons:

• Understanding variability: Variance helps to understand the extent of variability in a data set, providing insights into the distribution and consistency of the data.
• Risk assessment: In finance, variance is used to measure the volatility of investment returns, helping investors assess risk and make informed decisions.
• Statistical analysis: Variance is a foundational concept in statistics, used in various analyses, including hypothesis testing, regression analysis, and ANOVA (Analysis of Variance).

Impact of variance on analysis and decision-making

High variance

• Indicates high variability: Data points are spread out over a wide range, suggesting less predictability and higher uncertainty.
• Implications for risk: In finance, high variance in investment returns indicates higher risk, as returns are more volatile and less predictable.

Low variance

• Indicates low variability: Data points are close to the mean, suggesting more predictability and lower uncertainty.
• Implications for stability: In finance, low variance in investment returns indicates lower risk, as returns are more stable and predictable.

Example:
In portfolio management, an investor might compare the variance of returns for two different assets. An asset with lower variance might be preferred for risk-averse investors, as it indicates more stable returns.

Challenges and considerations in using variance

• Sensitivity to outliers: Variance can be heavily influenced by outliers, which can distort the measure of variability.
• Units of measurement: Variance is expressed in squared units, which can make interpretation less intuitive compared to the standard deviation (the square root of variance).
• Context-dependent: The significance of variance depends on the context and the specific application, requiring careful interpretation in different fields.

Example:
In quality control, a manufacturing company might use variance to monitor the consistency of product dimensions. High variance could indicate issues in the production process, prompting further investigation and corrective actions.

Understanding variance is essential for analyzing data variability, assessing risk, and making informed decisions in various fields. For further exploration, related topics include standard deviation, statistical analysis, risk management, and data distribution. These subjects provide deeper insights into the applications and implications of variance in different contexts.