Whether on purpose or not, a statistician can mislead an audience with a chart. This article explains some chart design principles and common mistakes novice data analysts make. Think about the statistical charts you have seen on billboards, in the news, and in research studies. Using these principles as a guide, would you classify any of those charts as misleading? Be sure to take note of the suggestions for successful dashboards.
It is more important to give emphasis to the data itself and sort your chart by data attributes, rather than non-data attributes (e.g., labels like country names).
With the data sorted, a proper comparison across the many bars is easier to do, as is finding a country in the list after a quick scan.
Pie charts work better when presented with sorted data values. Start at 12 o'clock with the largest slice and work clockwise. In this way, it is much easier to understand relations between parts - what is bigger and what is smaller - even when values are not readable, or areas are very similar.
If the chart is interactive, give the user the possibility to change the default sort order and a way to filter out data to compare only a few categories.