Figure 1-5, columns A and B, show the adverse affect of splitting the
scale. Column A appears to be about 1/2 the height of column B, which is
not the desired comparison. When you examine the scale closely and do
some simple math, you can determine that column A is actually 1/3 the
height of column B. Columns C and D give the correct comparison of the
columns because these columns use a better designed scale.
Figure 1-5.
Split scales give wrong proportion of the data
The data the chart presents has a direct bearing on the scale you select.
When determining the scale, you must pay particular attention to the
smallest value the chart shows. There are two extremes you should avoid:
choosing a scale with unrealistically small increments and choosing a
scale with unrealistically large increments. A well-designed chart scale
should fall somewhere between these two extremes.
For example, when a chart presents data with no quantity smaller than 100
units, then you should not use a scale with increments of 5 units. The
opposite of this statement is also true. If a chart presents data with
increments all below 25 units, you do not use a scale with increments of
50 units. You would find plotting the data using that scale extremely
difficult.
Examine figure 1-5 again. You can see the only change made to correct
the chart so columns C and D give the correct proportion was the scale.
The change doubled the value of the scale's graduations. Changing the
scale eliminated the need for splitting the scale, thus, the chart
presented the data accurately.
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