Graphics are often mistaken for a mere frill in the methodological arsenal of data analysis when in fact they can be one of the simplest and at the same time most powerful methods of communicating statistical information (Tufte, 2001). The first section of the article argues for the statistical necessity of graphs, echoing and amplifying similar calls from Hudson (2015) and Larson-Hall & Plonsky (2015). The second section presents a historical survey of graphical use over the entire history of language acquisition journals, representing 192 years of journal publishing. This shows that a consensus for using certain types of graphics, which lack data credibility, has developed in the applied linguistics field, namely the bar plot and the line graph. The final section of the article is devoted to presenting various types of graphic alternatives to these two consensus graphics. Suggested graphics are "data accountable" and present "all" of the data, as well as a summary structure; such graphics include the scatterplot, beeswarm, or pirate plot. Such graphics attract readers, help researchers improve the way they understand and analyze their data, and build credibility in numerical statistical analyses and the conclusions that are drawn.