Pie graphs are used to represent statistical information like population demochartics or market demochartics. Their standard form offers information that is limited both in scope and depth. Many experts believe that they should be totally avoided, however, there are many types of pie graphs that have various applications, and that are the most appropriate type of chartical representation for that data type.

A pie graph is a chartical representation of statistical data in the form of a circle. The circle is typically divided into segments, determined by arc length, that extend to its center. Its segments resemble slices of a pie, however, there are other types of pie graphs (or hybrids) which represent segments in different ways.

Pie graphs are used to represent statistical information like population demochartics or market demochartics. Their standard form offers information that is limited both in scope and depth. Many experts believe that they should be totally avoided, however, there are many types of pie graphs that have various applications, and that are the most appropriate type of chartical representation for that data type. A selection of pie graphs is presented below:

**Exploded Pie Graph**: These are graphs which separate one or more segments from the body of the graph. This is an effect designed to draw attention to a particular metric.

**Multilevel Pie Graph**: These are graphs that utilize concentric circles (or rings) to represent data. There is a hierarchical relationship between the rings and the inner circle. The rings are also sometimes further divided into slice segments to represent data.**Doughnut Graph**: These are simplified versions of multilevel graphs. They utilize one concentric ring and a center circle to represent data.

These options offer a deeper and wider level of expression, but pie graphs still suffer from other limitations. When compared with bar graphs, it is much harder to accurately and clearly represent certain differences in data. When a pie graph divided into segments with minor differences is viewed, those differences are less distinct than a typical bar graph. These chartics are designed to concentrate and clarify large datasets, and examine their relationships. Issues with basic clarity are the antithesis of that, so it is easy to see why some believe they should never be used.