Table 1.
Methods | Properties | Limitations | |
---|---|---|---|
One-dimensional plots |
ECDF | Nonparametric estimate of the cumulative distribution. All estimates are on the same scale Reveals differences in distributions. Easy to find the location of the quantiles |
Not good for visualizing the shape of the distributions. Not easy to adjust for other variables |
Histogram | Reveals most frequent values. Good for visualizing: the location of the distributions-multimodality and asymmetry |
Depends on user defined bandwidth. Not good to directly assess center and spread |
|
Boxplot | Substantial reduction of distribution. Good for visualizing: the relative location of the distributions–asymmetry. Samples are compared on a vertically aligned scale |
Relevant for unimodal distributions. Not good for visualizing the shape of the distributions. The x-axis is typically arbitrary |
|
Two-dimensional plots |
Scatterplot | Statistical summary of multidimensional data. Detection of outliers and their relationship (e.g., plate effect) |
Low throughput visualization. Comparison of many data sources is difficult (require multiple plots). Relevant for bivariate distributions (can be extended). |
2D view (contour plot) |
Deals with large number of data points. Detection of spatial variation and association |
Two-dimensional. Difficult to compare many different views |