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Statistics, like other subjects under the umbrella of mathematics, may be difficult for some people to translate into writing. But like anything else, statistical information needs to be properly communicated; hence the purpose of statistical writing. And whether you are working with group or alone in a statistics course, having a bit guidance in this area will not only save time and energy but hopefully avoid a lot of headache and frustration in the long run.

f you are comfortable in your English courses but suffer when it comes to writing scientifically or mathematically, this may be because you never really learned the differences between the two. Often times literature-rich subjects, such as English or History, enjoy colorful words, vivid descriptions, and sophisticated yet clever terminology in the body of their works. Mathematical, technical or scientific writing on the other hand, values clear and precise wording. Wording that is void of 'flowery' additions and works to communicate exactly what is intended - *vague terms are especially discouraged*.

Considering the special approach that needs to be taken for statistical or *mathematical writing*, some students may find themselves having a bit of trouble their first time around. Outside of the possible writing hindrances just mentioned, other common roadblocks to successful statistical writing may be;

If you've felt loss in class since day one, and don't understand the basic terms and reasons behind certain procedures, this alone can be a major barrier between you and your assignment.

When using statistical programs such as SPSS, the printouts from such programs can be very long and intimidating. Not knowing what's important and what's not can definitely delay the statistical writing process.

A student may have all the necessary data in their paper but due to inappropriate placement of it in the text (such as using computer printouts rather than original tables) may cost them a few points at grade time. Also wordy explanations that simply repeat all of the information in the table (rather than extract, analyze, and evaluate) may also fare poorly in the eyes of instructors.

Now, that you have a general idea of what *statistical writing* calls for, and some of the common obstacles students face, let's take a quick look at the two main types of statistics; inferential and descriptive.

For instance, if a sample of 3,000 citizens were asked certain questions about their race and income, the answers that they provided would then be applied to the entire US population. Though this may seem a bit unfair initially, several test of *significance* are ran to identify the likelihood of this sample of 3,000 citizens actually being a true representative sample of the entire US population. And often times the results are quite shocking!

Coming from its name, *descriptive*, this type of statistics refers to how you *describe* the population being studied. So you may use certain terms such as mean, median, mode, and frequency to talk about the data that you've collected. And because these tools often allow you to *summarize* information as well, descriptive statistics is also referred to as *summary* statistics.

Regardless of the type of statistics that you will be implementing into the writing portion of your statistic project (and its likely that you may use both), you'll need to know how best to present that data in the body of your paper. When bringing information of this nature to light, visual aids should be utilized to organize numbers as well as accurately relay what is intended.

Tables are excellent to use when there is a need to communicate test results or other relevant data. The original source of this information is usually computer generated and may come with lots of unnecessary information. When formulating tables its your job to extract the most relevant data for your audience and neatly organize it into a legible table. Some examples of information that may be placed in a table;

- standard deviation
- mean, median, and mode
- correlations
- frequencies
- p values
- specific test results (such as t-test, ANOVA, chi-square etc.)

Another popular means of relaying statistical information is through the use of a chart. There are several types of charts that you may run across and all usually deal with some type of plotting; such as, scatter plots, probability plots, and histograms. And even though you've probably never bothered to learn the exact names of these charts, they in fact are used all the time to display statistical data. Bar graphs as well (though not generally considered for 'plotting') are also utilized; usually to compare results.

So in most cases, you will use your statistical chart to either **(a) compare two or more things (b) or show a relationship**.

With regards to the first purpose, comparing, generally this will be done between two groups, such as comparing the mean or median of group1 versus the mean or median group2. This may be done in bar graph form and is an excellent means of displaying significant information. Likewise, relationships are often shown through plotting and will likely utilize an X, Y graph. These graphs may be used to summarize things such as correlation and regression analyses.

Finally, after you've displayed all of your data either through a table or chart, its important for you to explain your results. And as mentioned earlier, confusion surrounding this task can sometimes lead to students losing valuable points on their statistics papers.

The solution is simple. When writing; simply think "*short and concise*". Unlike English prose or other forms of writing, length is really not a bonus with statistical or mathematical writing. Avoid repeating all the information that is in the table or chart, and think critically about the data that is in front of you; extracting only what is necessary and worthy of being discussed.

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