## Writing with Inferential Statistics

**Summary:**

This handout explains how to write with statistics including quick tips, writing descriptive statistics, writing inferential statistics, and using visuals with statistics.

**Contributors:**Reuben Ternes**Last Edited:** 2012-02-28 10:46:22

#### Writing Statistics Plainly

In general, you should always 'translate' your statistics into some understandable form for your reader.

**Poor example:**"A t-test (t = 3.59) showed that the two groups were significantly different (p<0.01)."

The example above is complicated and hard to read. It's better to say something plainly first, then provide the statistical evidence afterwards:

**Better example:**Women scored higher than men on the aptitude test (t = 3.89, p < 0.01).

In the second example, your reader understands the relationship, it's not filled with jargon, but all of the same information is presented. Note that different fields have their own way of writing with statistics—please refer to your field's style guide for specific guidelines.

When using a complicated inferential procedure that your readers would be unfamiliar with, explain it. It may be necessary to go over it in detail. You may want to cite who used it first, and why they used it, and explain how it is applicable to your situation. A footnote or an appendix is a fine place for such an explanation.

If you include statistics that many of your readers would not understand, consider adding the statistics in a footnote or appendix if you can, especially if it is not central to your argument.

#### Writing Statistics Accurately

If you aren't sure how to calculate a particular statistic, either find out how, or don't use it. Along the same lines, never plug in numbers into a computer program, such as SPSS, and think that the output is "correct." Computer programs don't think for us; they simply allow for fast calculations. They cannot and do not interpret results. You should never interpret the results of a statistic that you don't fully understand. This is extremely important.

When in doubt, keep it simple. If the only thing you can say for certain is that the mean of one group is higher than the mean of another group, then that is fine. This is evidence, albeit it's not as strong as other types of evidence.

Remember that inferential statistics can never "prove" anything. You should think of statistics as a body of evidence (much like a fingerprint at a crime scene) that provides support for your argument. Sometimes it can be used as primary evidence or sometimes it is used in a more supporting role.

#### Focusing on Statistics

How you frame the use of your statistics is extremely important. In a more scientific field, you'll probably want your statistics as a focal point, but in other fields (say politics, for instance) you may use statistics to support a stance or policy, but it may be only one of many reasons for that policy. Knowing how your audience will react to statistics should affect how you use it. If your audience doesn't use a lot of statistics, you probably shouldn't make statistics the focal point of your argument, or if you do, you need to be very good about explaining the logic behind your statistics.