语言教学 | 普渡大学写作教学系列Research&Citation15-Writing with Statistics(1)

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Using Research Introduction

These OWL resources will help you use the research you have conducted in your documents. This area includes material on quoting and paraphrasing your research sources, as well as material on how to avoid plagiarism.

1、Writing with Statistics -- Overview and Introduction

Statistics is a tricky business. The casual reader doesn't understand statistics in any great depth, while the experienced reader often knows a lot about the subject. Balancing between these two extremes is often difficult, and far from natural. The following resource is meant as a guide to writing statistics.

This guide is not meant to teach you statistics, but rather how to use statistics more effectively in your writing. This guide is designed to help you understand both how to write using other people's statistics, and how to write using your own statistics. If you want to learn how to interpret statistics, then take a course taught by a professional. For an excellent beginner's textbook, see Introduction to the Practice of Statistics by David S. Moore and George P. McCabe.

What is a Statistic?

In the casual sense, a statistic is any number that describes a group of objects. There are two main categories of statistics, descriptive and inferential.

• Descriptive: Statistics that merely describe the group they belong to.

• Inferential: Statistics that are used to draw conclusions about a larger group of people.

Examples of Descriptive Statistics

The class did well on its first exam, with a mean (average) score of 89.5% and a standard deviation of 7.8%.

This season, the Big High School Hockey Team scored a mean (average) of 2.3 goals per game.

Many times, however this group of objects is a smaller subset of a larger group. By examining the smaller subset, it is often thought that information can be inferred upon the larger population. This is the basis of inferential statistics.

Examples of Inferential Statistics

According to our recent poll, 43% of Americans brush their teeth incorrectly.

Our research indicates that only 33% of people like purple cars.

In these last two examples, the researchers have not studied all people, they have studied a small group of people, and are generalizing the results to lots of people. This is known as inferential statistics, because you are inferring properties about a large group from a smaller group. As a statistician or a researcher, it is your hope that this smaller group is representative of the larger group, and that the two groups behave the same way. If they do not, then your inference may not be correct.

If you merely want to describe the data that you have for one single group, then you are using descriptive statistics. If you want to say something about a larger group, or you want your reader to infer something about a larger group, then you need to use inferential statistics. It is important to understand the difference between these two because how you use a statistic depends on what type of statistic it is.

This handout contains the following information:

• Overview and Introduction: A brief introduction to statistics and how to write with them.

• Quick Tips: An overall list of dos and don'ts about writing statistics.

• Descriptive Statistics: Statistics that are used to describe data.

• Writing Descriptive Statistics: Tips on writing descriptive statistics.

• Inferential Statistics: Statistics that are used to infer patterns in a population. This section includes some definitions, basic theory, and applications.

• Writing Inferential Statistics: Tips on writing inferential statistics. This section includes p-values and their interpretation.

• Using Visuals and Statistics: How to use visuals to represent statistics.

• Terms: Formal definitions of useful terms.

2、Quick Tips On Writing with Statistics

1. Never calculate or use a statistical procedure you don't fully understand. If you need a statistical procedure, and you don't understand it, then you need to consult someone or learn how to do it properly.

2. Never attempt to interpret the results of a statistical procedure you don't fully understand. If you need to interpret a particular statistic, talk with a professional statistician and make sure you understand the proper interpretation. Unlike descriptive statistics, inferential statistics is anything but black and white, there may be several valid interpretations of a given statistic, and you need to be aware of which ones are better under which circumstances.

3. If you are using statistics in a paper, consider your audience. Will they understand the statistics you are using? If not, you may need to explain the procedure that you are using in detail. This is not inappropriate! It is better to include too much information than too little. Depending on your field, this may be done using an appendix, footnotes, or directly in the text.

4. Present as much information as needed so that your reader can make their own interpretation of your data. Certainly, your job is to help the reader interpret your data, but most statistics are used to support a persuasive argument. You need to give your reader enough information that they can reconstruct your argument from your statistics. If you don't give them enough information, people will think that you are being deceptive, which can damage your credibility. You can't convince someone of anything if they are convinced that you are misleading them!

5. Use graphics and tables. Statistics can contain a lot of information, and using visuals can display a lot of information in a manner that can be quickly understood. See the section on visuals and statistics for more information.

6. If it's applicable, and you can calculate it, do include some measure of variability; typically this is a standard deviation. Even if you aren't doing any inferential statistics, this statistic provides excellent information about your data set.

7. Be wary of using statistics from other places that are not peer-reviewed. Popular magazines are notorious for including bad statistics. Oftentimes their 'sample' is a section of people who choose to respond to some online query. Their sample often includes mostly women or mostly men (depending on the magazine) but rarely do they have a good representation from all genders, and many times the magazines imply that the results generalize to the entire population. While some statistics might be generalizable, many are not. If it's not from a reliable source, then don't use it.

8. Speaking of sources, if you used a statistic, you need to provide your audience with additional information including where the statistic came from. You should be wary of statistics that seem to appear out of nowhere.

• A poor example: The ten largest cities in the U.S. comprised 54% of the total U.S. population.

• A good example: According to the United States Census Bureau, in 2000, the ten largest cities in the U.S. comprised 54% of the total U.S. population.

In the second example, your audience knows exactly where the statistic comes from (if they don't believe your statistic, they can go and check themselves) and it comes from a reputable source (the U.S. Census Bureau).

9. If you calculated a statistic, how did you calculate it? In some fields, you don't need to tell your readers how you calculated some statistics. For example, in psychology, you don't need to explain how you did an ANOVA or a t-test, but in other areas you might need to explain this in more detail.

10. Be clear as to what population(s) your statistic is meant to generalize to. If your sample included only male college students, you should be very careful if you want to generalize your results to female lawyers. Don't imply that your sample generalizes to everyone if your statistic was calculated from a more specific population.

11. If you are using inferential statistics, try to speak as plainly as possible, and put the statistics at the end of the sentence. See the Writing Inferential Statistics section for more information.

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