What are the two distribution shapes for non-normal standard deviation?

The correct answer is that the two distribution shapes for non-normal standard deviation are rectangular and triangular.

A rectangular distribution, often referred to as a uniform distribution, occurs when all outcomes are equally likely within a certain range. This means that each value in the distribution has the same likelihood of occurrence, resulting in a flat, rectangular shape when graphed.

A triangular distribution, on the other hand, reflects a scenario where values cluster around a particular mode, creating a peak in the center that tapers off towards the minimum and maximum values. This distribution resembles the shape of a triangle and is useful in scenarios where there is a known range of minimum and maximum values, and the likelihood of occurrence increases towards the midpoint.

Understanding these two shapes is crucial for analyzing data that does not fit the assumptions of normal distribution, as they provide alternative models for representing variability and uncertainty in data. This knowledge enables analysts to select appropriate statistical methods when dealing with various types of data distributions.