When visualizing quantitative data using a histogram, which of the following correctly describes the shape of the distribution?

• A. The distribution is always bimodal for quantitative data.
• B. The distribution can be described as $\text{symmetric}$, $\text{skewed left}$, or $\text{skewed right}$ depending on the data.
• C. The distribution is always $\text{uniform}$ regardless of the data.
• D. The shape of the distribution is not relevant for quantitative data.

Answer: (B)

Verified step by step guidance

1. Understand that a histogram is a graphical representation of the distribution of quantitative data, showing the frequency of data points within specified intervals (bins).
2. Recognize that the shape of the distribution in a histogram can vary depending on the data set; it is not fixed to one form such as always bimodal or always uniform.
3. Learn the common types of distribution shapes: symmetric (where the left and right sides are roughly mirror images), skewed left (longer tail on the left side), and skewed right (longer tail on the right side).
4. Note that the shape of the distribution is important because it provides insights into the nature of the data, such as central tendency, variability, and potential outliers.
5. Conclude that the correct description is that the distribution can be symmetric, skewed left, or skewed right depending on the data, making the shape relevant and variable for quantitative data.