Business Communications & Presentations

 Numbers are so much a part of your life that you probably pay little attention to them:  “The unemployment rate stands at 6.8 percent nationwide.”  “Odds are 1 in 100 that a baby born to a woman over 40 will have Down syndrome.”  “Housing starts are down 19 percent since last April.”

 Teams of people collect data from throughout the country  Facts were compiled  Sorted  Analyzed  News writers then look through the pages of information to come up with a few short, simple-sounding sentences  Remember: numbers can be put together in many different ways!

 A great deal of time is spent trying to make numeric data understandable  Statistics: the numerical data that business/government agencies gather & analyze  Are used to clarify information and help make predictions  Sometimes the numbers are arranged somewhat deceptively

 When no attempt is made to influence the reader, to make predictions, or to generalize, the facts are reported just as the occur  These are straightforward statements of fact:  Of the 65 married couples surveyed, 57 owned a dog  Of the 314 students in our graduating class, 11 were homeschooled until ninth grade

 Numerical data are usually gathered for a specific purpose  Those using the data often add words & phrases that make a certain point or influence the audience in some way  Journalists call this practice: editorializing  Trying to do more than simply inform  Used when someone is trying to sway the thinking of an audience in some way

 Of the 65 married couples surveyed, 57 owned a dog  Editorialized: Of the 65 married couples surveyed, nearly every one of them (57) owned a dog.  Of the 314 students in our graduating class, 11 were home-schooled until ninth grade.  Editorialized: Of the 314 students in our graduating class, 11, including 3 honors students, were home-schooled until ninth grade.

 Notice that the basic numerical facts have not been changed  But editorializing has subtly – and sometimes not so subtly – told readers how the writer wants them to think about the facts.

 Is important to a general audience to have an idea of what a number means  527,611,432,112 = unwieldy to use  More than 525 billion or nearly 530 billion – close enough to inform people in a factual way & to impress them with the quantity  Usually some editorializing is done when the rounded number is reported

 Ratio: a simple way of expressing the relationship between 2 sets of numbers  Example: the ratio of men to women in the U.S. is approximately 49 to 51  Percentages: actually a ratio in which everything is compared to 100  Percent means “per hundred”  If about 49 out of every 100 people in the U.S. are male, you can say that 49% of the U.S. population are male

 Public opinion polls are good examples of using smaller numbers of people to represent larger groups.  It is usually impossible to count everyone or everything to get the desired information  Example:  In this opinion poll, a representative sampling of 1,000 people were asked whether they believed that women’s professional sports will attract large enough crowds and sufficient media coverage within the next ten years to pay the athletes competitive salaries and to provide sports careers for large numbers of young women in the United States. (Margin of error is ± 4 percent).

 1,000 people would have to represent a cross section of the people in order to get an idea of how the American people felt  That means:  That the men & women surveyed would have to be from all walks of life  From high, middle, & low income levels  From a variety of ethnic & religious backgrounds  Their responses would be generalized to the rest of the population  This technique is acceptable & fairly accurate when a large enough sample is used & appropriate statistical formulas are followed

 Often unnoticed by the average reader or listener – the sampling error is given.  Example: “the sampling error was plus or minus 5%”  This a way of saying that the data aren’t guaranteed to be 100% accurate  Generalizations can never account for 100% of reality

 Different from a representative sample  How is a random sample obtained?  One old-fashioned ways is by lot  Example: to an idea of how students at a certain school with an enrollment of 850 felt about a certain issue – put name of each student in a large bin  Draw names one at a time until, say, 150 students’ names were on a list (17.6%)

 The 17.6% could be used to predict how the rest of the students feel about the issue  Chances are:  Every grade would be represented  As well as both sexes  Students from a variety of family backgrounds  A variety of neighborhoods  Some would be active in sports – some would not  Point: All students would have had an equal chance of having their names drawn

 Example: researcher uses the telephone directory as a sampling of a particular city  Pick every 50 th or 150 th name until reach total number needed for sampling  Then researchers survey those people by phone  With modern technology, computers use large databases to produce true random lists for research purposes

 Ad based on a survey of dentists:  50% of Dentists Surveyed Prefer Glossy toothpaste!  Does it tell how many dentists were surveyed?  Possibly only 4 dentists were surveyed  2 out of 4 is 50%  Do you think that many readers might read such a statement & believe that 50% of all dentists prefer this toothpaste?

 Target Group: a subset of the population who would be most likely to provide useful information about the topic in question – possibly a group that has special knowledge or opinions about a subject  Example:  If you want to find out how 13-year-olds feel about being a teenager, who would you ask?  Their parents?  No! you ask the 13-year-olds