## Using Statistics and Data in Your Speech

### Learning Objectives

Explain how to use statistics and data in your speech.

Using statistics in public speaking can be a powerful tool. It provides a quantitative, objective, and persuasive platform on which to base an argument, prove a claim, or support an idea. Before a set of statistics can be used, however, it must be made understandable by people who are not familiar with statistics. The key to the persuasive use of statistics is extracting meaning and patterns from raw data in a way that is logical and demonstrable to an audience. There are many ways to interpret statistics and data sets, but not all of them are valid.

### Guidelines for Helping Your Audience Understand Statistics

• Use reputable sources for the statistics you present in your speech such as government websites, academic institutions and reputable research organizations, and policy/research think tanks.
• Use a large enough sample size in your statistics to make sure that the statistics you are using are accurate (for example, if a survey only asked four people, then it is likely not representative of the population’s viewpoint).
• Use statistics that are easily understood. Many people understand what an average is but not many people will know more complex ideas such as variation and standard deviation.
• When presenting graphs, make sure that the key points are highlighted and the graphs are not misleading as far as the values presented.
• Statistics is a topic that many people prefer to avoid, so when presenting statistical idea or even using numbers in your speech be sure to thoroughly explain what the numbers mean and use visual aids to help you explain.

### Communicating Statistics

Graphs, tables, and maps can be used to communicate the numbers, but then the numbers need to be put into context to make the message stick.

### Putting Statistics into Context for Our Audiences

We are so used to resorting to statistics that we tend to bombard our audiences with too many mind-numbing numbers. As Chip and Dan Heath state:

Statistics are rarely meaningful in and of themselves. Statistics will, and should, almost always be used to illustrate a relationship. It’s more important for people to remember the relationship than the number.[1]

We need to put statistics into context for our audiences. In their book, the Heaths give several good examples of others who have done this. For example, they introduce us to Geoff Ainscow, one of the leaders of the Beyond War movement in the 1980s.

Ainscow gave talks trying to raise awareness of the dangers of nuclear weapons. He wanted to show that the U.S. and the USSR possessed weapons capable of destroying the earth several times over. But simply quoting figures of nuclear weapons stockpiles was not a way to make the message stick. So, after setting the scene, Ainscow would take a BB pellet and drop it into a steel bucket where it would make a loud noise. The pellet represented the bomb that was dropped on Hiroshima. Ainscow would then describe the devastation at Hiroshima. Next, he would take 10 pellets and drop them in the bucket where they made 10 times as much noise. They represented the nuclear firepower on a single nuclear submarine. Finally, he poured 5,000 pellets into the bucket, one for each nuclear warhead in the world. When the noise finally subsided, his audience sat in dead silence.

That is how you put statistics into context.

### Using Tables, Graphs and Maps to Communicate Statistical Findings

The story of communicating your statistics does not end with putting them into context. Actually, it would be better to say that it does not begin with putting the numbers into context. In reality, the story you are telling through your evidence will probably start with the display of a table, graph, or map.

A simple table, graph, or map can explain a great deal, and so this type of direct evidence should be used where appropriate. However, if a particular part of your analysis represented by a table, graph, or map does not add to or support your argument, it should be left out.

While representing statistical information in tables, graphs, or maps can be highly effective, it is important to ensure that the information is not presented in a manner that can mislead the reader.

### A deeper dive: Maps and the electoral college

As this short video from Vox points out, every election cycle we see a map of electoral votes that fails to illustrate—and even conceals—the way electoral votes actually work. In the video (at 1:15), they show a map-based chart by the New York Times that conveys this crucial information much more clearly and accurately than a geographically accurate map of the states. This example should serve as a reminder that maps, like charts, exist to convey information. If the information you’re conveying isn’t captured in a geographic representation—if you’re describing population, for instance, rather than land mass—you might consider a different way to make that information visible to your audience.

The key to presenting effective tables, graphs, or maps is to ensure they are easy to understand and clearly linked to the message. Ensure that you provide all the necessary information required to understand what the data is showing. The table, graph, or map should be able to stand alone.

Tables, graphs, and maps should relate directly to the argument, support statements made in the text, summarize relevant sections of the data analysis, and be clearly labeled.

### Table Checklist

• Use a descriptive title for each table.
• Label every column.
• Provide a source if appropriate.
• Minimize memory load by removing unnecessary data and minimizing decimal places.
• Use clustering and patterns to highlight important relationships.
• Use white space to effect.
• Order data meaningfully (e.g., rank highest to lowest).
• Use a consistent format for each table.

Also, do not present too much data in tables. Large expanses of figures can be daunting for an audience, and can obscure your message.

### Graph Checklist

• Use a clear, descriptive title.
• Choose the appropriate graph for your message, avoid using 3D graphs as they can obscure information.
• Decide which variable goes on which axis, and what scale is most appropriate.
• If there is more than one data series displayed, always include a legend, preferably within the area of the graph.
• All relevant labels should be included.
• Colors can help differentiate; however, know what is appropriate for the medium you’re using.
• Provide the source of data you’ve used for the graph.
• For readability, it’s generally a good rule of thumb to make the y-axis three-quarters the size of the x-axis.

### A deeper dive: STorytelling with Data

1. Heath, Chip, and Heath, Dan. Made to Stick: Why Some Ideas Survive and Others Die. Random House, 2007, 133.