This reading teaches me how to determine data graph that is effective and which one is not. Especially this graph with heavy shadow, this graph is what I usually see often in school and universities; Furthermore, it is not only that, even the graph from Microsoft Excel is what we usually work with is actually hard to read and it is finally explained that it is a ‘bad perception’ (1.2.3 Bad perception) which leads to misleading data.
When it comes to data visualization, it needs a good visual for audience to perceive informations. I noticed that there is a lot of similarity of making a good data graphic with graphic design formula that I have learned, such as contrasts, grids and colors. It makes your graph easier to understand since it has a lot of information in it.
However, theres a possibility of bad data happening as well. It is written on the article,
‘In your everyday work you will be in little danger of producing either a “Monstrous Costs” or a “Napoleon’s Retreat”. You are much more likely to make a good-looking, well-designed figure that misleads people because you have used it to display some bad data. Well-designed figures with little or no junk in their component parts are not by themselves a defence against cherry-picking your data, or presenting information in a misleading way. Indeed, it is even possible that, in a world where people are on guard against junky infographics, the “halo effect” accompanying a well-produced figure might make it easier to mislead some audiences. Or, perhaps more common, good aesthetics does not make it much harder for you to mislead yourself as you look at your data.’ - 1.2.2 Bad Data
It is aesthetically pleasing to the eye but however, it causes data misleading as well.In conclusion, data visualization needs to be presented very clear with brief information indicated as well