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The field of data and information visualization is of interdisciplinary nature as it incorporates principles found in the disciplines of [[descriptive statistics]] (as early as the 18th century),<ref>{{cite journal|last1=Grandjean|first1=Martin|author1-link= |title=Data Visualization for History|journal=Handbook of Digital Public History|date=2022|volume=|issue=|pages=291–300|doi=10.1515/9783110430295-024|isbn=9783110430295 |url=https://shs.hal.science/halshs-03775019/document}}</ref> [[visual communication]], [[graphic design]], [[cognitive science]] and, more recently, [[interactive computer graphics]] and [[human-computer interaction]].<ref>{{Citation |title=A Framework for Visualizing Information |author=E.H. Chi |publisher=Springer Science & Business Media |year=2013 |page=xxiii}}</ref> Since effective visualization requires design skills, statistical skills and computing skills, it is argued by authors such as Gershon and Page that it is both an art and a science.<ref name="Gershon">{{cite journal |last1=Gershon |first1=Nahum |last2=Page |first2=Ward |title=What storytelling can do for information visualization |journal=Communications of the ACM |date=1 August 2001 |volume=44 |issue=8 |pages=31–37 |doi=10.1145/381641.381653|s2cid=7666107 }}</ref> The neighboring field of [[visual analytics]] marries statistical data analysis, data and information visualization and human analytical reasoning through interactive visual interfaces to help human users reach conclusions, gain actionable insights and make informed decisions which are otherwise difficult for computers to do.
Research into how people read and misread various types of visualizations is helping to determine what types and features of visualizations are most understandable and effective in conveying information.<ref name="Mason">{{Cite journal |first1=Betsy |last1=Mason |title=Why scientists need to be better at data visualization |url=https://knowablemagazine.org/article/mind/2019/science-data-visualization |journal=Knowable Magazine |date=November 12, 2019 |doi=10.1146/knowable-110919-1 |doi-access=free|url-access=subscription }}</ref><ref name="O'Donoghue">{{cite journal |last1=O'Donoghue |first1=Seán I. |last2=Baldi |first2=Benedetta Frida |last3=Clark |first3=Susan J. |last4=Darling |first4=Aaron E. |last5=Hogan |first5=James M. |last6=Kaur |first6=Sandeep |last7=Maier-Hein |first7=Lena |last8=McCarthy |first8=Davis J. |last9=Moore |first9=William J. |last10=Stenau |first10=Esther |last11=Swedlow |first11=Jason R. |last12=Vuong |first12=Jenny |last13=Procter |first13=James B. |title=Visualization of Biomedical Data |journal=Annual Review of Biomedical Data Science |date=2018-07-20 |volume=1 |issue=1 |pages=275–304 |doi=10.1146/annurev-biodatasci-080917-013424 |url=https://www.annualreviews.org/doi/full/10.1146/annurev-biodatasci-080917-013424 |access-date=25 June 2021|hdl=10453/125943 |s2cid=199591321 |hdl-access=free }}</ref> On the other hand, unintentionally poor or intentionally misleading and deceptive visualizations (''misinformative visualization'') can function as powerful tools which disseminate [[misinformation]], manipulate public perception and divert [[public opinion]] toward a certain agenda.<ref>{{Citation |title=Misinformed by Visualization: What Do We Learn From Misinformative Visualizations? |author1=Leo Yu-Ho Lo |author2=Ayush Gupta |author3=Kento Shigyo |author4=Aoyu Wu |author5=Enrico Bertini |author6=Huamin Qu}}</ref> Thus data visualization literacy has become an important component of [[data literacy|data]] and [[information literacy]] in the [[information age]] akin to the roles played by [[literacy|textual]], [[numeracy|mathematical]] and [[visual literacy]] in the past.<ref>{{Citation |author1=Börner, K. |author2=Bueckle, A. |author3=Ginda, M. |year=2019 |title=Data visualization literacy: Definitions, conceptual frameworks, exercises, and assessments |journal=Proceedings of the National Academy of Sciences |volume=116 |issue=6 |pages=1857–1864|doi=10.1073/pnas.1807180116 |doi-access=free |pmid=30718386 |bibcode=2019PNAS..116.1857B |pmc=6369751 }}</ref>
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