These two charts portray the exact same data yet are viewed as individually distinct graphs due to the nature in which the information is displayed. This example of lying through charts presents the notion that we, as students, must pay close attention to the axis’s scaling because information can be manipulative in the explanation of data and therefore we cannot interpret graphs without gaining a complete understanding of what it really means and where it comes from.
Chart 1:
Chart 2:
The two charts posted are of the same information but give off two very different impressions to viewers. Chart 1 has shorter range of values represented on the chart, which gives off the impression that the growth in percentage is large. Being that the purpose of charts and graphs is to demonstrate the difference or growth from one point to another, this graph would benefit someone who was trying to make small growth seem larger than it really is. The 2nd chart does just the opposite. It shows the same set of data but on a larger scale that shows the difference from one point to another on a larger scale. This type of chart makes the data change appear to be minimal compared to that of the 1st chart. The owner of chart 2 would not want to display it because it would emphasize minimal growth of their product, for example.
The above charts illustrate the malleability of statistics; both charts represent identical data. On the first chart, the vertical axis ranges from 40 to 80, the extremes of the data. On the second chart, the vertical axis rages from -10 to 100. The change in scale significantly effects the slope of the graph. The steep slope gives the appearance that the data is changing rapidly while more moderate slope give the impression that the data is relatively stable. The line on the second chart was also smoothed, making the change look even less significant. This exercise demonstrates how easily statistics can be manipulated to give the reader a certain idea. It is also a caution to me, as a reader, to be wary about the presentation of data.
In this exercise we manipulated statistics by altering chart settings in order to make the data lie for our purposes. In the first chart we show the data in its original range. It looks like there is a large positive correlation between the percentage and the year because as each year increases by 10 the percentage also goes up. When we change the range on the y axis for example if we make the minimum 0 and the maximum 800 the correlation between x and y looks alot less significant. If I wanted to make it seem like the percent had changed over time I would use the first graph. If I wanted to prove the opposite than I would use the second graph.
Both of these graphs depict the same information correctly. However, visually, the shape of the data is quite different. Curve manipulation is possible through the variability of the y-axis range. By changing the vertical axis field limits, the graph itself changes. In Version 1 of the data, the y-axis min/max scale has been calibrated to [40, 80]. The “slope” of the curve is steep, implying a drastic change. However, in Version 2 of the data set, the vertical axis limits have been set to [0, 100], which produces a flattened shape of the curve, which suggests a mellow increase. Here, two graphs depicting identical data sets can be represented in notably different ways, showing how the y-axis scale technique can be used to distort statistical details.