Misleading Graph

Misleading Graph

• It is a well known fact that statistics can be misleading. They are often used to prove a point and can easily be twisted in favour of that point.

• Good graphs are extremely powerful tools for displaying large quantities of complex data; they help turn the realms of information available today into knowledge. But, unfortunately, some graphs deceive or mislead.

• This may happen because the designer chooses to give readers the impression of better performance or results than is actually the situation. In other cases, the person who prepares the graph may want to be accurate and honest, but may mislead the reader by a poor choice of a graph form or poor graph construction.

• The following things are important to consider when looking at a graph:

1. Title

2. Labels on both axes of a line or bar chart and on all sections of a pie chart

3. Source of the data

4. Key to a pictograph

5. Uniform size of a symbol in a pictograph

6. Scale: Does it start with zero? If not, is there a break shown

7. Scale: Are the numbers equally spaced?

• A graph can be altered by changing the scale of the graph. For example, data in the two graphs of Fig. 2.6.1 are identical, but scaling of the Y-axis changes the impression of the magnitude of differences.

Example 2.6.1: Construct a frequency distribution for the number of different residences occupied by graduating seniors during their college career, namely: 1, 4, 2, 3, 3, 1, 6, 7, 4, 3, 3, 9, 2, 4, 2, 2, 3, 2, 3, 4, 4, 2, 3, 3, 5. What is the shape of this distribution?

Solution:

Normal distribution: The normal distribution is one of the most commonly encountered types of data distribution, especially in social sciences. Due to its bell-like shape, the normal distribution is also referred to as the bell curve.

Histogram of given data: