Distribution Skewness Explained

The question describes a distribution that is skewed to the left. This characteristic is a key indicator of the distribution's shape.

Understanding Left Skewness

A distribution skewed to the left is also known as a negatively skewed distribution. Key features include:

• The tail of the distribution extends towards the lower values (to the left on a number line).
• The bulk of the data points, or scores, are concentrated at the higher end of the scale.
• The mean is typically less than the median, which is typically less than the mode.

The description mentions "many high scores," which directly aligns with the concentration of data points towards the higher end characteristic of a left-skewed distribution.

Evaluating Distribution Types

• Evenly distributed distribution: This is a symmetrical distribution with no skew.
• Platykurtic distribution: This describes the 'flatness' or peakedness of a distribution's curve (kurtosis), not its skewness.
• Negatively skewed distribution: This matches the description of being skewed to the left with data concentrated at higher values.
• Positively skewed distribution: This distribution is skewed to the right, with the tail extending towards higher values and data concentrated at lower values.

Therefore, a distribution that is skewed to the left and has many high scores is a negatively skewed distribution.