The term "distribution shape" refers to the visual representation of how data points are spread across a range of values. Common shapes include normal distribution, which is bell-shaped and symmetrical, and skewed distribution, where data points cluster more on one side. Understanding the shape helps in analyzing data patterns and making predictions. Different distribution shapes can indicate various underlying processes. For example, a uniform distribution shows equal frequency across values, while a bimodal distribution has two peaks, suggesting two different groups within the data. Recognizing these shapes is essential for effective data analysis and interpretation.