With apologies to the authors, we provide the following list of the top ten worst graphs in the scientific literature. As these examples indicate, good scientists can make mistakes.

1. Roeder K (1994) DNA fingerprinting: A review of the controversy (with discussion). Statistical Science 9:222-278, Figure 4

[The article | The figure | Discussion]

3. Epstein MP, Satten GA (2003) Inference on haplotype effects in case-control studies using unphased genotype data. American Journal of Human Genetics 73:1316-1329, Figure 1

[The article | The figure | Discussion]

4. Mykland P, Tierney L, Yu B (1995) Regeneration in Markov chain samplers. Journal of the American Statistical Association 90:233-241, Figure 1

[The article | The figure | Discussion]

5. Hummer BT, Li XL, Hassel BA (2001) Role for p53 in gene induction by double-stranded RNA. J Virol 75:7774-7777, Figure 4

[The article | The figure | Discussion]

6. Cawley S, et al. (2004) Unbiased mapping of transcription factor binding sites along human chromosomes 21 and 22 points to widespread regulation of noncoding RNAs. Cell 116:499-509, Figure 1

[The article | The figure | Discussion]

7. Kim OY, et al. (2012) Higher levels of serum triglyceride and dietary carbohydrate intake are associated with smaller LDL particle size in healthy Korean women. Nutrition Research and Practice 6:120-125, Figure 1

[The article | The figure | Discussion]

9. Cotter DJ, et al. (2004) Hematocrit was not validated as a surrogate endpoint for survival amoung epoetin-treated hemodialysis patients. Journal of Clinical Epidemiology 57:1086-1095, Figure 2

[The article | The figure | Discussion]