Not all statistics are created equal. You can ask the following questions in order to evaluate the reliability of a statistical data set.
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Authenticity |
Who published the data? What was their motivation for publishing it? |
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Authority |
What are the qualifications and reputation of the author(s) of the data set? |
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Date |
What is the date range of the data? Is it historical or current? |
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Content |
How is the data presented? Is it clear? |
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Accuracy |
Can the statistics be verified? Do the methods used and data presented seem valid? |
There is an almost overwhelming amount of statistical data available on the web. In this guide you will find a number of links to different agencies that produce statistics. The following list includes some of the biggest agencies that appear throughout the guide. These agencies all produce statistics on a wide range of topics.
Data visualizations are ways that data can be visually represented and presented, such as charts and graphs. There is an increasing number of data visualizations on the web that are innovate and even artistic.
You can use data visualizations to help you better understand large data sets, or you can create your own visualizations to help others understand your data.
You can also find statistics in peer-reviewed, academic journal articles in your discipline. The following are a few of the databases that feature statistical information.
GOOD is a magazine and an online hub that acts as "a collaboration of individuals, businesses, and nonprofits pushing the world forward." One of GOOD's best features is the Transparency graphics site, which displays visually interesting statistical data about issues ranging from social justice to history.
The Google Public Data Explorer makes large data sets easy to explore, visualize and communicate. You can run a public data search or browse the available data sets to see visualizations of data at the county, state, national, or global level.
Watch this video to learn more.
Learn more about data visualization and the data visualization community, and see examples of snazzy looking data.
