1/11/2024 0 Comments Infographic show innacurate![]() ![]() Review Figure 2.15 below, which is a 3D bar graph of the percentage of Canadian vs. Two common issues are: distortion and occlusion. Three-dimensional (3D) data visualizations may look visually appealing, but they often make it more difficult to interpret the data and spot patterns within them. A line graph showing an overall upward trend on gasoline prices in Canada over a longer timeframe (May 2019-November 2021). When looking at the full timeline (i.e., long timeframe), the reader can see that gasoline prices are increasing in Canada. Now review Figure 2.14 below, which shows an overall upward trend on gasoline prices in Canada from May 2019 to November 2021. A line graph showing a downward trend on gasoline prices in Canada over a short timeframe (May 2019-February 2020). Because of the carefully selected timeframe (i.e., short timeframe), it appears that the gasoline prices in Canada are decreasing. ![]() Review Figure 2.13 below, which shows a downward trend on gasoline prices in Canada from May 2019 to February 2020. For example, showing an upward sales trend over the first few months of a year, while omitting the data that showed sales declined for the rest of the year. This can create a false impression of the data. The term “cherry-picking” refers to only presenting the best data, and omitting data points which are less favourable, in order to reinforce a particular narrative. A line graph showcasing the price of sugar in Canada with a more compressed scale. A line graph showcasing the price of sugar in Canada with an expanded scale. This makes the data appear less significant than it could really be (see Figure 2.12 below for a more compressed scale).įigure 2.11. Because of the expanded scale on the line graph, there does not appear to be much fluctuation in the cost of sugar in Canada. Review Figure 2.11 below, which shows the cost of sugar in Canada from January to July 2021. Compressing or expanding the scale of a graph can make the changes between data points seem either more or less significant than they really are. It’s important to examine the scales of a data visualization carefully. A line graph of the number of nectarines produced and automotive apprenticeship registrations in Canada appears to show a relation, as they both begin to decrease in 2019, where there is none. What do these two things have to do with each other? They are unrelated quantities that appear to decrease at the same rate over a similar time period. Review Figure 2.10 below, which shows a line graph of the decrease of Canadian automotive apprenticeship registrations and nectarine production. It means that, just because two trends seem to fluctuate alongside each other, it doesn’t prove that one causes the other or that they are related in a meaningful way. If you’ve ever taken a statistics or data analysis course, you have almost certainly come across this common phrase. A pie chart displaying 12 categories of television viewing in Ontario in 2004 provides too much visual information, making it hard to read. There are 12 categories of television and similar colours used in the graph, as well as white font over the bright colours, making this hard to read. Review Figure 2.9 below, a pie chart of Ontario television viewing in 2004. For example, pie charts are good for making comparisons between a few different categories, but are not great for identifying patterns or showing data over time.ĭata visualizations can be confusing and misleading when the designer has picked a format that isn’t well suited to the data they are analyzing. ![]() Using the Wrong Type of Data VisualizationĪs we learned in Module 1, some types of data visualizations work well for communicating specific types of information, but not others. Now that we know how to analyze and break down a data visualization, let’s go through a few examples of design choices (and mistakes!) that can create confusion. Module 2: How to Critically Analyze and Interpret Data Visualizations ![]()
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