%0 Journal Article %1 Sperandei2014 %A Sperandei, Sandro %D 2014 %J Biochemia medica %K BiomedicalResearch BiomedicalResearch:methods BiomedicalResearch:standards BiomedicalResearch:statistics&numericaldata Brazil Brazil:epidemiology ComputerGraphics ComputerGraphics:standards DataInterpretation Humans InformationDissemination InformationDissemination:methods Leprosy Leprosy:epidemiology Neoplasms Neoplasms:epidemiology PracticeGuidelinesasTopic Publishing Publishing:standards Questionnaires Questionnaires:standards ResearchDesign ResearchDesign:standards ResearchDesign:statistics&numericaldata Statistical %N 3 %P 311-20 %R 10.11613/BM.2014.033 %T The pits and falls of graphical presentation. %U http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4210251&tool=pmcentrez&rendertype=abstract %V 24 %X Graphics are powerful tools to communicate research results and to gain information from data. However, researchers should be careful when deciding which data to plot and the type of graphic to use, as well as other details. The consequence of bad decisions in these features varies from making research results unclear to distortions of these results, through the creation of "chartjunk" with useless information. This paper is not another tutorial about "good graphics" and "bad graphics". Instead, it presents guidelines for graphic presentation of research results and some uncommon, but useful examples to communicate basic and complex data types, especially multivariate model results, which are commonly presented only by tables. By the end, there are no answers here, just ideas meant to inspire others on how to create their own graphics.

@article{Sperandei2014, abstract = {Graphics are powerful tools to communicate research results and to gain information from data. However, researchers should be careful when deciding which data to plot and the type of graphic to use, as well as other details. The consequence of bad decisions in these features varies from making research results unclear to distortions of these results, through the creation of "chartjunk" with useless information. This paper is not another tutorial about "good graphics" and "bad graphics". Instead, it presents guidelines for graphic presentation of research results and some uncommon, but useful examples to communicate basic and complex data types, especially multivariate model results, which are commonly presented only by tables. By the end, there are no answers here, just ideas meant to inspire others on how to create their own graphics.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Sperandei, Sandro}, biburl = {https://www.bibsonomy.org/bibtex/20d1385b9d99e85c5d47e42903be66ddc/jepcastel}, doi = {10.11613/BM.2014.033}, interhash = {23cff3a6541e5a3b58736fe0e3af1d78}, intrahash = {0d1385b9d99e85c5d47e42903be66ddc}, issn = {1330-0962}, journal = {Biochemia medica}, keywords = {BiomedicalResearch BiomedicalResearch:methods BiomedicalResearch:standards BiomedicalResearch:statistics&numericaldata Brazil Brazil:epidemiology ComputerGraphics ComputerGraphics:standards DataInterpretation Humans InformationDissemination InformationDissemination:methods Leprosy Leprosy:epidemiology Neoplasms Neoplasms:epidemiology PracticeGuidelinesasTopic Publishing Publishing:standards Questionnaires Questionnaires:standards ResearchDesign ResearchDesign:standards ResearchDesign:statistics&numericaldata Statistical}, month = {1}, note = {Presentació de dades; Introductori}, number = 3, pages = {311-20}, pmid = {25351349}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {The pits and falls of graphical presentation.}, url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4210251&tool=pmcentrez&rendertype=abstract}, volume = 24, year = 2014 }