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, not all of them valid.
Some common uses of statistics in a speech format may include:
A common misunderstanding when using statistics is "correlation does not mean causation." This means that just because two variables are related, they do not necessarily mean that one variable causes the other variable to occur. For example, consider a data set that indicates that there is a relationship between ice cream purchases over seasons versus drowning deaths over seasons. The incorrect conclusion would be to say that the increase in ice cream consumption leads to more drowning deaths, or vice versa. Therefore, when using statistics in public speaking, a speaker should always be sure that they are presenting accurate information when discussing two variables that may be related. Statistics can be used persuasively in all manners of arguments and public speaking scenarios—the key is understanding and interpreting the given data and molding that interpretation towards a convincing statement.
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.
Credibility makes our messages believable, and a believable message is more likely to be remembered than one that is not. But gaining credibility is not so easy. As Chip and Dan Heath note in Made to Stick:
If we’re trying to persuade a skeptical audience to believe a new message, the reality is that we’re fighting an uphill battle against a lifetime of personal learning and social relationships.
So how can we add credibility to our words? One way is to rely on statistics.
We are so used to resorting to statistics that we tend to bombard our audiences with too many mind-numbing numbers. As the Heaths 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.
We need to put statistics into context for our audiences. In the 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 US 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.
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. 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:
Also, do not present too much data in tables. Large expanses of figures can be daunting for an audience, and can obscure your message.
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