The distribution, which is skewed to the left, has one high point and many high scores, is called:

Correct option is C

A negatively skewed distribution is characterized by:

• Skewness to the left: The tail of the distribution is longer on the left side.
• High scores concentrated on the right side of the graph.
• One high point or peak representing the mode, which is higher than the mean and median.

For example, a negatively skewed distribution might appear in data like exam scores where most students perform well, but a few score significantly lower.

Information Booster:

1. Characteristics of Skewed Distributions:
• Negatively skewed: Mean < Median < Mode (tail on the left).
• Positively skewed: Mode < Median < Mean (tail on the right).
2. Skewness:
• A measure of asymmetry in a dataset.
• Indicates whether data points cluster more toward one side of the distribution.
3. Real-Life Examples of Skewness:
• Negative skew: Retirement ages, where most people retire around 60-65 but a few retire much earlier.
• Positive skew: Income distribution, where most people earn lower wages but a few earn very high salaries.

Additional Information:

Evenly Distributed Distribution (Option 1):

·         Describes a uniform distribution where values are evenly spread across the range.

·         No skewness exists, as the distribution is symmetrical and balanced.

  Platykurtic Distribution (Option 2):

·         Refers to a distribution with flat peaks and less pronounced tails, indicating low kurtosis.

·         Kurtosis relates to the shape and sharpness of the peak, not the skewness or asymmetry of the distribution.

    Positively Skewed Distribution (Option 4):

·         This distribution has a tail extending to the right, opposite to the negatively skewed distribution.

·         Most scores are low and clustered on the left, while a few higher scores stretch the tail to the right.