Savvy Info Consumers: Data & Statistics

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What is Data?

Data is information collected during research.  Data can be analyzed, interpreted, and/or evaluated using statistical, mathematical, and other procedures to answer research questions.  

Data can be quantitative (numerical) and/or qualitative (text, images, sound, etc.)

This is an example of raw data within an Excel spreadsheet.

This image shows raw data (and not statistics) because:

• Analysis, interpretation, and/or evaluation hasn't occurred.  
• Headings and cells may be difficult to understand without more detail regarding the research.  

Data Source:  Social Explorer Tables: ACS 2015 (5-Year Estimates) (SE), ACS 2015 (5-Year Estimates), Social Explorer, U.S. Census Bureau.

What are Statistics?

Statistics are data that have been analyzed, interpreted, and/or evaluated.  Statistics are often presented in aggregate form and visualized in tables, charts, maps, graphs, etc.

This is an example of statistics presented in a bar graph.

This image shows statistics (and not data) because:

• Analysis, interpretation, and/or evaluation has occurred.
• The information (data) is summarized and visualized.

Source: Barthel, M., Mitchell, A. & Holcomb, J. (2016). Many Americans Believe Fake News is Sowing Confusion.  Pew Research Center. 

ASK YOURSELF: Is this source SMART?

Determine if your data/statistics are SMART before believing what is reported.    

Source: Who or what is the source?

• Who collected the data?
• Can you trace the data back to the original source?  The data source and the reporter/citer/publisher are not always the same (e.g. advocacy organizations often publish data that were produced by some other organization).  
• What are the credentials of the original source?
• If the source is unclear, be skeptical about the data/statistics.    
• Make sure it's a source you can trust - e.g. Pew Research Center.  

Motive: Why do they say so?  

• What was the purpose of the collection or study?
• Who was the intended audience for or users of the data/statistics?
• Was it collected as part of the mission of an organization, for advocacy, or for business purposes?  
• Do they have a special interest or particular point of view that may cause them to slant data/statistics to suit their beliefs or causes?
• Biased sources can be accurate, but you need to check them carefully.

Authority: Who produced the data/statistics?

• What are the author's/data producer's credentials?
• How widely known or cited is the producer?
• Who else uses the data?
• Is the producer an expert on the subject?
• If an individual, what organizations are they associated with?  Could that association affect the work?

Review: Go over the collection methods & completeness carefully.  

• What methodology was used?  Is the methodology documented?   
• How are the data collected - count, measurement, or estimation? 
• If a survey:
  • What was the total population?  How does that compare to the size of the population it is supposed to represent?
  • What methods were used to select the population included?  How was the population sampled?
  • What populations were included and/or excluded?
  • What was the response rate?    
• Are there any notable errors in facts or conclusions?  
• Is the information current enough?  Remember that there is often a delay between data collection and data reporting because analysis and publication take time.  

Two-source Test: Double check everything if possible.

• Do other sources provide similar numbers? 
• Research the subject in journal articles and newspapers, by interviewing others, and search online.  
• Does your two-source test confirm or contradict the data/statistics?

Read Data & Statistic visualizations carefully.

Read charts, tables, and infographics carefully.  

• What do the numbers, percentages and averages represent?  
• Are the x and y axes labeled at an appropriate scale for the data?  Are the distances between each point on the axis equal?  Does the y-axis start at zero?
• Look at the numbers.  Don't take the visualization at face value.  

Consider the two infographic examples at the bottom of this guide.

For more info, read Infographics Lie.  Here's how to spot the B.S.

Credits:  Content on this page was adapted from:

• Jylisa Doney and Alicia Kubas' Finding Data & Statistics research guide at North Dakota State University (Accessed March 13, 2017).
• Laurence McKinley Gould Library's Data, Datasets, and Statistical Resources research guide at Carleton College (Accessed March 13, 2017).