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In Dougherty et al. “Sheff v O’Neill: Weak Desegregation Remedies,” the following data is given to show the progress toward the Sheff I  goal during in the years 2003-2007.

When plotted on a line chart, the data can show a progression either minor or significant. All depends on how the chart is formed.

This first chart portrays the data in a way that shows minor progress with a relatively flat line. The effect is created by using a large range of percentages along the vertical axis, fixing the minimum at .0 (0%) and the maximum at 1.0 (100%).

In contrast, this chart portrays the data in a way that shows significant progress with a more steeply sloped line. The effect is achieved by using a small range of percentages along the vertical axis, fixing the minimum at .1 (10%) and the maximum at .3 (30%).

Both charts represent the same set of data. However, their difference in showing the progress, either minor or significant, is an example of how charts can be used to lie or otherwise give the reader a false impression of what the data means.

In Dougherty et al. “Sheff v O’Neill: Weak Desegregation Remedies,” the following data is given to show the progress toward the Sheff I  goal during in the years 2003-2007.

When plotted on a line chart, the data can show a progression either minor or significant. All depends on how the chart is formed.

This first chart portrays the data in a way that shows minor progress with a relatively flat line. The effect is created by using a large range of percentages along the vertical axis, fixing the minimum at .0 (0%) and the maximum at 1.0 (100%).

In contrast, this chart portrays the data in a way that shows significant progress with a more steeply sloped line. The effect is achieved by using a small range of percentages along the vertical axis, fixing the minimum at .1 (10%) and the maximum at .3 (30%).

Both charts represent the same set of data. However, their difference in showing the progress, either minor or significant, is an example of how charts can be used to lie or otherwise give the reader a false impression of what the data means.

Data can easily be skewed in order to prove or disprove a point or area of research. When looking at progress towards the Sheff I goal, information portrayed in a graph can be situated to look like schools are making either grand leaps in progress, or small steps. After taking data from Figure 5.1, titled “Actual and Legal Progress toward Sheff I Goal, 2003-2007″, and making it into a line graph, it is easy to see that data can be skewed in order to prove a point.

Looking at the graph to the right, it looks like schools are not making a lot of progress in regards to meeting the 2006-07 goal. The slope of the line is not very steep because the y-values are separated by increments of 20%. This makes the progress line seem horizontal and slow-moving. It looks like it will take some time before schools meet their goal of 30%. I made this graph by utilizing the “Insert Chart” option on Google Spreadsheet. By fiddling with the minimum and maximum values, along with the scale of the y-axis, I made the graph look like schools are not making progress towards meeting the 2006-07 Sheff I goals.

Looking at the graph to the left, one would assume that the schools are making steady progress towards meeting the 2006-07 goals. I purposely did not include the “Goal” line of 30% integration because I wanted to make the line as steep as possible (within reason). In order to do this, I made the y-interval scale .1 and only showed five percentage points. Since the y-interval maximum was 14%, the line goes beyond the graph, which makes it seem like schools are making great progress. If one were to glance at this map without looking closely at the y-intervals, one would assume that schools are making progress.

I found this exercise very helpful in terms of understanding how people can play with graphs in order to prove their point.

If you have ever looked at a chart, you probably made assumptions about what exactly it was telling you, according to what it looked like. By manipulating the scales of the x and y axis, charts that display the same data can be perceived very different. This data, of the progress of Sheff I can look two different ways; it can look like there was a ton of progress, and it can look like there was very little progress. The data I am working with is:

 

 

 

 

 

With this, we can make the data look like this:

 

 

 

 

 

 

Or, look like this:

 

 

 

 

 

 

When looking at these two charts, we see two different stories. By changing the scales, we see that there was a lot of progress towards the goal (chart 1) or that there was little progress towards the goal (chart 2). Those that use charts to show statistics can make the data look different based on the way scales are used. To make the data look different, I changed the Y-scale to 0-100% rather than 0-35%. By doing this, the data looks entirely different and tells two different stories. I created these charts through excel by putting data into a spreadsheet and then creating a chart according to that data. All people have to do with charts showing statistics is change the maximum and minimum values of the Y-scale and the chart tells something different.

Different types of graphs, with different scales, can portray the same set of data in many different ways.  These three images show how one can take the same information but use it in different ways in order to convince people there has been significant change, or almost no change at all.

 

The graphs that I made below both use the above bar graph as their data, but appear convey very different messages. One shows very minor changes, which would be used if one wanted to show how little progress has been made.  The second one has a very steep curve, used if one wanted to display an extreme change in percentage of minority students in reduced isolation settings.

 

See how this graph has a scale on the Y axis that goes up to 100. This leads to a very horizontal line–one that looks as if almost no change has happend.  Using a bigger scale is one of the ways people can “lie with statistics” in order to prove the point that they want.

 

This graph on the other hand starts at 8 instead of zero, and ends at just 18 on the Y axis. This leads to a very vertical line, so it looks like there has been a very high increase in percentage of students in reduced isolation settings.  One would use a small scale on the Y axis if they want to falsely prove a point that there has been a lot of change.

 

I was surprised how drastic the difference in graphs was just by changing the axis. So, in conclusion, when looking at graphs, one should always check the numbers on the axis and think about the scale the author is using before coming to conclusions about graphs.