Statistics

Introduction

While making claims in your writing, it is critical to include statistics and data. You will want to find numbers, percentages, and facts that were derived from reputable studies. Many people may have a tendency to look for data to prove their hypothesis or idea, as opposed to really answering their research questions. However, you may find that the opposite happens: the data may actually disprove your hypothesis. You should never try to manipulate data so that it gives credence to your desired outcome. While it may not be the answer you wanted to find, it is the answer that exists. You may, of course, look for other sources of data – perhaps there are multiple sources of data for the same topic with differing results. Inconclusive or conflicting findings do happen and can be the answer (even if it’s not the one you wanted!).

Conflicting results on the same topic are common. This is the reality of research because, after all, the questions researchers are studying are complicated. When you have conflicting results you can’t just ignore the differences—you’ll have to do your best to explain why the differences occurred. To have the most accurate data supporting your claims, you must do a thorough evaluation of the relevance and credibility of your sources.

Examples

Sometimes data is actually necessary to answer research questions, particularly in the social sciences and life and physical sciences. For instance, data would be necessary to support or rule out these hypotheses:

• More women than men voted in the last presidential election in a majority of states.
• A certain drugs shows promising results in the treatment of pancreatic cancer.
• Listening to certain genres of music lowers blood pressure.
• People of certain religious denominations are more likely to find a specific television program objectionable.
• The average weight of house cats in the United States has increased over the past 30 years.
• The average square footage of supermarkets in the United States has increased in the past 20 years.
• More tomatoes were consumed per person in the United Kingdom in 2015 than in 1962.
• Exploding volcanoes can help cool the planet by spewing sulfur dioxide, which combines with water vapor to make reflective aerosols.

Evaluating Statistics

Evaluating data for relevance and credibility is just as important as evaluating any other source. Another thing that is the same with data is that there is never a 100% perfect source. There will be times that you’ll have to make educated guesses (inferences) about whether the data are good enough for your purpose.

Critical thinking as you evaluate sources is something your professors will expect. But you’ll benefit in other ways, too, because you’ll be practicing a skill necessary for the rest of your life, both in the workplace and in your personal life. It’s those skills that will keep you from being duped by fake news and taken advantage by posts that are ignorant or, sometimes, simply scams.

To evaluate data, you’ll need to find out how the data were collected. If the data are in another source, such as a book; web page; or newspaper, magazine, or research journal article, evaluate that source in the usual way. If the book or newspaper, magazine, or web page got the data from somewhere else, do the same evaluation of the source from which the book or article got the data. The article, book, or web page should cite where the data came from. If it doesn’t, then that is a black mark against using that data. (The data in a research journal article are often the work of the authors of the article. But you’ll want to be sure they provide information about how they collected the data.)

Then determine the data’s relevance to your research question by considering such questions as:

• Were the data collected recently enough?
• Is the data cross-sectional (based on information from people at any one time) or longitudinal (based on information from the same people over time)? If one is more appropriate for your research question than the other, is there information that you can still logically infer from this data?
• Were the types of people from whom the data were collected the same type of people your research question addresses? The more representative the study’s sample is of the group your research question addresses, the more confident you can be in using the data to make your argument in your final product.
• Was the data analysis done at the right level for your research question? For instance, it may have been done at the individual, family, business, state, or zip code level. But if that doesn’t relate to your research question, can you still logically make inferences that will help your argument? Here’s an example: Imagine that your research question asks whether participation in high school sports in Columbus City Schools is positively associated with enrolling in college. But the data you are evaluating is analyzed at the state level. So you have data about the whole state of Ohio’s schools and not Columbus in particular. In this case, ask yourself whether there is still any inference you can make from the data.

To evaluate the credibility of the data in a research journal article you have already read, take the steps recommended in Evaluating Sources, plus consider these questions:

• Is the article in a peer reviewed journal? (Look at the journal’s instructions for authors, which are often located on the journal’s website, to see if it talks about peers reviewing the article and asking for changes [revisions] before publishing.) If it is a peer reviewed journal, consider that a plus for the article’s credibility. Being peer reviewed doesn’t mean it’s perfect; just more likely to be credible.
• Do the authors discuss causation or correlation? Be wary of claims of causation; it is very difficult to determine a causal effect. While research studies often find relationships (correlation) between various variables in the data, this does not equal causation. For instance, let’s return to our example above: If the study of Ohio high schools students’ sports participation showed a positive correlation between sports participation and college enrollment, the researcher cannot say that participation caused college enrollment. If it were designed to show cause and effect, the study would not have resulted in a correlation. Instead, it would have had to have been designed as an experiment or quasi-experiment, used different statistical analyses, and would have supported or not supported its hypotheses.

Incorporating Statistics

Once you have located data that you believe is credible and relevant, you must incorporate the data points in your essay using APA format. Because writing is personal and cyclical, you might approach this with an initial draft, a revision exercise to spot areas that could use more factual support, locate sources, and then come back a final time while editing to put everything in proper APA format. A peer review or an instructor’s review of one of your drafts will help you obtain additional feedback on your claims, the support you used to back them up, and your use of APA format.

Summary

To support a claim in a college-level essay, you need to incorporate reputable sources. Data from research studies, peer-reviewed articles, and recent publications should be deeply analyzed for relevance, accuracy, and applicability to your chosen topic.

While you may locate research that refutes your claim, you should not view this as a lost opportunity. Instead, incorporating both supportive and refuting information can be useful if you are able to uncover flaws in the refuting data.

Data in all forms, including charts, graphs, tables, and figures has a prescribed format in APA. Please be sure to follow an APA manual while reviewing your sources for the proper use of APA.

“Choosing & Using Sources: A Guide to Academic Research.” By Teaching & Learning, Ohio State University Libraries. Retrieved from: https://ohiostate.pressbooks.pub/choosingsources/chapter/data-as-sources/ Licensed under: CC-BY 4.0. Adapted by The American Women’s College.