**Funnel plot**  A plot designed to visualize for the existence of natural variance due to differences in the population sizes of each study, even though the same variable is measured. It assumes that samples drawn from large populations will be plotted near the average, and studies from small populations will be spread evenly on both sides of the average, creating a roughly funnel-shaped distribution. Deviation from this shape can indicate true signal, such as the Glasgow datapoint above (for bowel cancer cases in the UK in 2008 per district, and taken from the Art of Statistics). **Slope chart** to compare pairs of ranked variables:  **Bump chart** to compare multiple rankings (which are similar to Alluvial diagrams below, but without an area/quantity):  The **Alluvial diagram** can be used to visualize quantitative relationships across variables and among their categories. The diagram is similar to a *Sankey chart* (see [Visualize flow](Visualize%20flow.md)), however, the nodes get ordered by rank, creating wavy and/or overlapping flows.  When the flows are straight and vertical, the Alluvial diagram is also known as **Parallel Sets**, and again is very helpful to understand relationships among variables. For example, it can be used to visually analyze the variables of the classic Titanic dataset:  **Correlogram** heat maps (see [Part-to-whole charts](Part-to-whole%20charts.md)) can be used to visualize variance across variables and display patterns or correlations among them.  And you can combined **heat maps** or bar charts with [table charts](Depict%20a%20single%20value.md) to compare distinct categorical variables:  **Spider charts** (aka, **Polar** or **Radar charts**) are useful if you want to compare multiple configurations of the same set of variables side by side, to identify the the strongest differences. E.g., in the plot below analyzing the Beatles, it is easy to see how the red Ringo polygon is very different while John and Paul are fairly similar: