You may decide to use statistics in your research for any number of reasons. When you use and present statistics, be sure they do the following:
Statistics are often easier to locate using print sources than the Internet. This is especially true if you are trying to locate older data, general numbers (as opposed to a specific piece of information), or the same set of statistics for multiple locations. Print sources have a number of other advantages over the Internet. They are:
A problem with Internet research is that data providers tend to create information that they post temporarily. This means that the information you’re looking at doesn’t really exist anyplace but on your computer screen. If you cite a web address in your paper, others might not be able to find the information if it is temporary. Seeing ‘cgi-bin’ in the web address is a tip-off to this practice.
Regardless of where you find your statistics, you need to evaluate their source. Statistical data is only as good as the people who create it. When you look at statistical data, you should be able to answer the following questions:
If you can’t answer these questions, look at the data source more closely to see if it is reputable. Your instructor or a librarian can help evaluate the source.
Even when numbers are accurately related, the selection of which numbers to provide or how to display them can have an impact on how they are perceived. To be complete, statistics should provide the basics. Look for numbers that provide supporting information. Percents can be misinterpreted if you don’t also have the underlying number.50% of a group of 100 is a lot different from 50% of a group of 2.
Just like citations of text, citations for numbers should be complete and provide enough information for others to find and verify the original source. Be wary of studies with incomplete citations. They may cite unidentifiable sources (and therefore unverifiable) with phrases like “statistical sources show…” or "studies say…” Tracking data back to an official source may give you additional information and help you to identify bias.
When you use statistics, you need to be able to explain why you selected the statistics you did and what they mean. This is especially important in academic work. If you can, provide a graphic presentation to help your reader understand the data you selected. Remember, someone will be evaluating your statistics in the same way you evaluated others. Make sure you do the following:
Statistics are invaluable as evidence in support of conclusions. If you can either find or generate statistics that show the truth of your conclusions, there are few that would refute your ideas. Most viewpoints use statistics to support their ideas. It’s your job to examine all statistics supporting all points of view and then arrive at your own conclusions based on all of them. If you can’t arrive at a conclusion through the statistics you have gathered, do your own study.
It is essential to know what the statistics are measuring. Most people will say that you can’t compare apples and oranges. This is both true and false. It all depends on what you are measuring. If you are measuring the color, texture, or appearance of oranges and apples there may be no similarities, but if you are measuring sugar content or vitamin content, there may be some correlation. You just need to know what you are measuring. Do not just put in numbers that seem to apply to your topic.
You should find out how the research was done. Many studies are done by asking people their opinions or what they do, think, or feel. These studies include political, sociological, consumer behavior, media audience, and other areas which are based on individual people’s ideas, opinions, and/or attitudes. These areas are often referred to as “soft sciences”, as opposed to “hard sciences” that do research designed to minimize as much as possible the human factor in the evidence and conclusions. The “human factor” is impossible to eliminate as long as humans are involved. For a study to be “scientific,” it must be repeatable and predictive in nature.
It is the soft sciences that most often misuse or misapply statistics. The studies are often not repeatable and usually not predictive. What people say or do is the basis of the statistics. What does this mean to you as you examine the statistics you plan on using as evidence? Try to determine whether the statistics are hard or soft science based. The simplest way to do this is to determine if people or nature is being studied. If it’s nature, it’s hard science. If it’s people, it’s soft science. If the statistics are based on hard science, check to see what results other researchers who have repeated the study obtained. Use the results that are consistent overall.
You should examine statistics to determine what are the comparisons being drawn and are they relevant and valid. For example, say your topic is gun control. You could find statistics on murder rates with handguns per capita in New York City, Tokyo, and London. Such statistics would show much higher rates in New York than the other two cities. It would appear that gun control is a good idea. However, such statistics must be suspect – not because they are wrong, but because they don’t tell the whole story. New York has an extremely stringent weapons control law (the Sullivan Act). Since this is the case, what happens to the argument that control laws work? There must be something else influencing the murder rate. Statistics on murder rates may say nothing about the efficacy of gun control laws, but rather about the cultural and/or societal factors that make such laws ineffective. For these reasons, you must search for other evidence to support whatever statistics you use.
Statistics are excellent evidence. They are often the easiest and most concise way to express what you are trying to say. You must examine all statistics for relevance, validity and authority. If you fail to do this, your statistics may do more harm than good in proving your point.
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