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Writers use data in their writing to provide evidence supporting their arguments and to help inform or persuade readers of their point of view.
As a critical reader, you should understand the data so you can evaluate how accurately the writer uses them.
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Types of Data: Quantitative and Qualitative
Quantitative Data
Quantitative data are numerical data that can be measured and analyzed using statistical methods. As a result, quantitative data are reported in numbers, percentages, averages, and so on. Examples of quantitative data include statistics, survey results, and scientific experiments.
An example of a sentence used to report qualitative data is
Researchers compared the performance of 350 college students using two note-taking methods, Method A and Method B, while studying for their exams. Results of the study revealed that students using Method A earned a lower average test score of 75.2 compared to those using Method B, who earned an average test score of 83.1.
Qualitative Data
Qualitative data are non-numerical data used for information that cannot be easily measured or analyzed statistically. Qualitative data are valuable to show, for example, trends in public opinions and behavior or to reveal characteristics of groups of people.
Typically, qualitative data are reported using quotes that illustrate or provide evidence to the writer’s main point. Examples of qualitative data include personal and expert testimonies, interviews, answers to questionnaires, and observations.
Mixed Data
Writers often mix different types of data. For example, you may read statistical results of a study (quantitative data) followed by a quote by an expert interpreting the results of the study (qualitative data). The writer then uses the information as evidence supporting their claims, opinions, or speculations.
How Data Are Presented
Depending on their purpose, writers may present data differently. They may use use tables showing large amounts of statistics, which they then explain in the text. To summarize information and highlight particular results, they may use charts, graphs, or infographics.
Evaluating the Data Presented as Evidence
Critical readers evaluate the information presented as evidence when they read because they do not want to be misled and because they want to make their own decisions
Biased Data
Writers have biases when they write. Interestingly, data can also be biased. In statistics, bias refers to data that do not represent people accurately.
For example, let’s say a group of people were asked to rate how much they liked various types of music. If only younger people were included in the study, the results might be very different if people of all ages were included.
As a reader, you should analyze studies that writers use to support their claims. Careful writers are likely to include enough information about a study they report in their writing. If they provide insufficient information about how a study was conducted, their conclusions may be invalid; You should not believe claims based on data that you cannot evaluate carefully.
Read the Original Source
When writers cite studies in their work, they summarize the study so that they select only the portions that are useful to support their claims. If needed, check the original studies to make sure the writer has not left out important information that may invalidate conclusions based on the data they presented.
Check the Source of the Data
Good data are likely to come from scientific publications and research reports. Make sure the source of information is credible and reliable.
Drawing Bad Conclusions from Good Data
Writers can use good data and still draw bad conclusions. They may be misguiding their readers on purpose; however, they often are simply not trained to understand research data themselves.
Research is complicated, the average reader cannot be expected to know everything about data and statistics, but some basic understanding can go a long way.
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Overgeneralization is a common way writers draw bad conclusions based on good data. For example, let’s say a study in the United States finds that a group of Spanish-language learners had good results from intensive conversation classes. A writer uses the study to claim that intensive conversation classes are key to learning languages. Such a conclusion is an overgeneralization, that is, it assumes that the same instructional strategy should work on students in other countries and on those learning other languages.
Misunderstanding the nature and limitations of the data also leads to drawing bad conclusions. For example, a study that finds that people who reported eating a banana every day had fewer colds than people reporting rarely eating no bananas. Thus, a correlation was found between eating bananas and having colds. A writer uses the study to claim that eating bananas helps prevent colds.
The problem with this conclusion is that the study found a correlation; perhaps those who eat bananas everyday are health conscious people and eat other fruits and vegetables compared to the other group. The study found a correlation, but the writer concluded incorrectly that eating bananas caused people to have fewer colds. A correlation should never be used to support a cause-effect relationship.
Dated information can be misleading, so make sure to check when the data writers present were produced. For example, if you read an article stating that the solar system has 9 planets, check the date. The text was very likely published before 2006, when Pluto was downgraded from a planet to a dwarf planet. Knowledge can change very quickly; claims based on old data can be invalid.
Summary: Reading strategies to understand data when reading
- Do not believe anything writers claim just because they used statistical data as evidence. You must still evaluate how well the data presented supports their claims.
- Make sure the data writers used to support claims are current and come from valid and reliable sources.
- Get information from all the information presented, not only what is in the text. Analyze tables, graphs, illustrations, infographics, and so on.
- Evaluate the data and try to detect overgeneralizations or misunderstandings based on statistical data.
- If needed, check the original sources of the data writers used to support their claims. This way, you will not rely only on what the writers report about the research.
Up Next: Analyzing the Argument
Go to the next lesson to learn about how to analyze the writer’s argument.