Data Literacy

Traits of Good Data

Good data has specific traits:

Accuracy & Precision
- Exactness of the data
- Accuracy when inputting the data
- Design of survey--ensuring that input is accurate and limited when appropriate

Reliability & Consistency
- Data consistent with other known sources
- Data fields consistent across all research
- Same “language” is used

Timeliness & Relevance
- Most up-to-date data
- Data used makes sense for research topic and time

Completeness & Comprehensiveness
- Enough information to draw conclusions.
- Data takes into account all possible information needs

Avaibility & Accessibility
- The data needed is available.
- Access to data is provided to fit the user’s role or the goals or mission of the organization.

Methods of Data Manipulation

People manipulate data using a variety of techniques, sometimes on purpose.

Cherry Picking

When only select evidence is presented in order to persuade the audience to accept a position, and evidence that would go against the position is withheld.

Sampling Bias

Sampling bias is an error related to the way the survey respondents are selected because the sample is not completely random. For example, sampling on social media excludes those who don’t use social media.

Hawthorne Effect

The Hawthorne Effect is the inclination of people who are the subjects of an experimental study to change or improve the behavior being evaluated because it is being studied and not because of changes in the experiment parameters or stimulus

Confirmation Bias

Confirmation bias is the tendency to search for, interpret, favor, and recall information in a way that confirms or supports one's prior beliefs or values.

False Causality

Correlation is not causation! To falsely assume when two events occur together that one must have caused the other.

Manipulation of Data Presentation

People manipulate the presentation of data to suit their bias and goals, many times on purpose.

Traits of Good Data Presentation:

Graphs should have a clear, self‐explanatory title
The units of measurement should be stated
Graphs should be simple and not too cluttered
All axes should be carefully labelled
Graphs should include the source of the data
The scale on each axis should not distort or hide any information.
Graphs should clearly show trends or differences between the data
Use two‐dimensional designs
Graphs should be accurate in a visual sense (e.g. if one value on the chart is 15 and another 30, then the second value should appear to be twice the size of the first)
Avoid abbreviations and acronyms
Color use should be consistent. For example, if males are shown in blue and females in red, this convention should be followed across all charts.

Questions to Ask Yourself

Does the claim match the data?
Does the claim see plausible?
What comparison is being made?
Who is making the claim?
How was the data gathered?
What’s missing?
Is the data being distorted?

“You don't have to burn books to destroy a culture. Just get people to stop reading them.”

Ray Bradbury

Voorhees High School Library Media Center | Leslie Edwards, Librarian

256 Country Road 513, Glen Gardner, New Jersey 08826

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