Home
Back
Pearson's Skewness Formula:
| From: | To: |
Pearson's Index of Skewness, also known as Pearson's moment coefficient of skewness, measures the asymmetry of a probability distribution. It quantifies the degree and direction of skew in a dataset relative to the normal distribution.
The calculator uses Pearson's Skewness formula:
Where:
Explanation: This formula compares the mean and median to detect asymmetry. When mean > median, distribution is positively skewed; when mean < median, distribution is negatively skewed.
Details: Skewness is crucial in statistics for understanding data distribution characteristics. It helps identify whether data is normally distributed or skewed, which affects statistical analyses and modeling decisions.
Tips: Enter the mean, median, and standard deviation values. Standard deviation must be greater than zero. The result is dimensionless and indicates skew direction and magnitude.
Q1: What do different skewness values indicate?
A: Positive values indicate right skew (tail to right), negative values indicate left skew (tail to left), and zero indicates symmetrical distribution.
Q2: What is considered a significant skewness value?
A: Generally, values between -0.5 and 0.5 indicate approximately symmetric distribution, while values beyond ±1 indicate highly skewed distributions.
Q3: How does skewness affect statistical analysis?
A: Skewed data may violate assumptions of parametric tests, requiring data transformation or non-parametric methods for accurate analysis.
Q4: Are there other measures of skewness?
A: Yes, including Fisher-Pearson standardized moment coefficient and Bowley's quartile coefficient of skewness.
Q5: When is Pearson's skewness most appropriate?
A: It works best with unimodal distributions and when the mean and median are meaningful measures of central tendency.