1. What Is Skewness?

Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. It describes the extent to which a distribution differs from a normal distribution in terms of symmetry.

2. How Does The Calculator Work?

The calculator uses the skewness formula:

Where:

• \( x_i \) — Individual data values (unitless)
• \( mean \) — Mean of all data values (unitless)
• \( n \) — Number of data points (count)

Explanation: The formula calculates the third standardized moment, measuring the degree of asymmetry in a distribution.

3. Importance Of Skewness Calculation

Details: Skewness is important in statistics and data analysis as it helps identify the shape of data distribution. It's used in various fields including finance, research, and quality control to understand data patterns.

4. Using The Calculator

Tips: Enter numerical values separated by commas. All values should be unitless as skewness is a dimensionless quantity.

5. Frequently Asked Questions (FAQ)

Q1: What does positive skewness indicate?
A: Positive skewness indicates a distribution with an asymmetric tail extending toward more positive values.

Q2: What does negative skewness indicate?
A: Negative skewness indicates a distribution with an asymmetric tail extending toward more negative values.

Q3: What is considered a normal skewness value?
A: For a perfectly symmetrical distribution, skewness is 0. Values between -0.5 and 0.5 are generally considered approximately symmetrical.

Q4: How many data points are needed for accurate skewness calculation?
A: While there's no fixed minimum, more data points provide a more reliable estimate of skewness. At least 20-30 data points are recommended.

Q5: Are there different types of skewness formulas?
A: Yes, there are several formulas for skewness including Pearson's first and second coefficients, but the third moment formula shown here is the most commonly used.