## Evaluating Data and Statistics

**Do the statistics/data come from a reliable source?**- Who collected the data, can you trace the data back to the original source, does the data source have any potential biases or conflicting interests, why were the data collected, and what were the data used for?

**Do the data/statistics fit your needs?**- Knowing whether you need a large data set to conduct a statistical analysis for a course, data on a specific topic to back-up or refute a hypothesis for an assignment, or statistics to support a larger argument will change the types of data/statistics you are looking for and whether or not what you've found meets your needs.

**How was the data collected for your data set or for the related statistics?**- What methodology was used and how was the sample and sample size chosen?

**Does the documentation and/or codebook define all variables?**- It is important to find sources that contain detailed documentation and/or codebooks which describe methodology, variables, as well as abbreviations for variable names.
- When working with raw data in statistical programs (such as Excel, SPSS, SAS, R, etc.) the documentation and/or codebooks can be the difference between having data that you can use and analyze, and data that cannot be understood.
- Depending on your subject area, different terms might be used to describe different concepts, so knowing exactly what the statistics describes will help you make stronger connections to your argument.

**Are the data or statistics visualizations (charts, graphs, maps, etc.) represented in a misleading way?**- 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?
- Remember to look at the actual numbers and not take the visualization at face value to get the best understanding of the data or statistic!

## Get Help

Questions about Data and Statistics? Contact Jylisa Doney or Alicia Kubas.

You can also ask for help by using the chat window.