Editing Box plot (section)

== Visualization ==
[[File:Boxplot vs PDF.svg|thumb|upright=1.2|Figure 7. Box-plot and a [[probability density function]] (pdf) of a Normal N(0,1σ<sup>2</sup>) Population]]

Although box plots may seem more primitive than [[histogram]]s or [[kernel density estimation|kernel density estimates]], they do have a number of advantages. First, the box plot enables statisticians to do a quick graphical examination on one or more data sets. Box-plots also take up less space and are therefore particularly useful for comparing distributions between several groups or sets of data in parallel (see Figure 1 for an example). Lastly, the overall structure of histograms and kernel density estimate can be strongly influenced by the choice of [[Histogram#Number of bins and width|number and width of bins]] techniques and the choice of bandwidth, respectively.

Although looking at a statistical distribution is more common than looking at a box plot, it can be useful to compare the box plot against the probability density function (theoretical histogram) for a normal N(0,''σ''<sup>2</sup>) distribution and observe their characteristics directly (as shown in Figure 7).

[[File:Boxplots with skewness.png|thumb|Figure 8. Box-plots displaying the skewness of the data set]]
{{clear}}