Here is a visual representation of how different skewness values affect data distribution:

1. Symmetrical Distribution (Skewness = 0): The first plot shows a normal distribution, where the data is symmetrically distributed around the mean.

2. Positive Skew (Right-Skewed): The second plot shows a positively skewed distribution, where most of the data is concentrated on the left, but the right tail extends further.

3. Negative Skew (Left-Skewed): The third plot shows a negatively skewed distribution, where most data is on the right, and the left tail extends further.

Would you like more details or have any questions?

Here are 5 related questions:

1. How does skewness affect statistical measures like mean and median?
2. What are examples of real-world data with positive skewness?
3. How can you quantify the amount of skewness in a dataset?
4. How does skewness relate to kurtosis in data distribution?
5. What transformations can you apply to normalize skewed data?

Tip: Skewness often affects measures of central tendency; in skewed data, the mean and median differ significantly.