1. What Is Negative Skewness?

Negative skewness (also called left-skewed distribution) occurs when the left tail of a distribution is longer or fatter than the right tail. It indicates that the majority of data points are concentrated on the right side of the distribution, with fewer extreme low values.

2. How Does The Calculator Work?

The calculator uses the skewness formula:

Where:

• \( Mean \) — Arithmetic average of the dataset
• \( Median \) — Middle value of the dataset
• \( SD \) — Standard deviation, measuring data dispersion

Interpretation: A negative result indicates negative skewness (left-skewed), a positive result indicates positive skewness (right-skewed), and values close to zero suggest a symmetrical distribution.

3. Importance Of Skewness Calculation

Details: Skewness measurement is crucial in statistics for understanding data distribution shape, identifying outliers, selecting appropriate statistical tests, and making accurate inferences about the population.

4. Using The Calculator

Tips: Enter the mean, median, and standard deviation values. All values must be valid (SD > 0). The result will show the skewness coefficient with interpretation.

5. Frequently Asked Questions (FAQ)

Q1: What does negative skewness indicate?
A: Negative skewness indicates that the distribution has a longer left tail, meaning most values are concentrated on the right with fewer extreme low values.

Q2: What is considered a significant skewness value?
A: Generally, skewness values between -0.5 and 0.5 are considered approximately symmetrical, values between -1 and -0.5 or 0.5 and 1 are moderately skewed, and values beyond -1 or 1 are highly skewed.

Q3: When is this formula most appropriate?
A: This formula works best for unimodal distributions that are not extremely skewed and have a moderate sample size (n > 30).

Q4: Are there other methods to calculate skewness?
A: Yes, other methods include Pearson's first coefficient of skewness and the Fisher-Pearson standardized moment coefficient, which may be more appropriate for different distributions.

Q5: How does skewness affect statistical analysis?
A: Skewness can affect the validity of parametric tests that assume normality. Highly skewed data may require transformations or non-parametric tests for accurate analysis.