Badge Overview

Statistics  Statistics

Published Public {} Badge Class Data

Statistics

Statistics

Issued by Hood College

Badge Description

The Statistics badge encompasses basic statistical methods as they apply to education and other fields. Topics include frequency distributions and their representations, measures of central tendency and dispersion, elementary probability, statistical sampling theory, testing hypotheses, non-parametric methods, linear regression, correlation and analysis of variance.

Skills statistics Probability Data Analysis

Badge Criteria

This FOUNDATIONAL level badge is equivalent to a 3-credit, master's level course. Earning a grade of B or better is required for this badge.

1. Understand why data beat anecdotes. 2. Understand why variability is natural, predictable and quantifiable. 3. Understand why random sampling allows results of surveys and experiments to be extended to the population from which the sample was taken. 4. Understand why random assignment in comparative experiments allows cause-and-effect conclusions to be drawn. 5. Understand why association is not causation. 6. Understand why statistical significance does not necessarily imply practical importance, especially for studies with large sample sizes. 7. Understand why finding no statistically significant difference or relationship does not necessarily mean there is no difference or no relationship in the population, especially for studies with small sample sizes. 8. Recognize common sources of bias in surveys and experiments. 9. Recognize how to determine the population to which the results of statistical inference can be extended, if any, based on how the data were collected. 10. Recognize how to determine when a cause-and-effect inference can be drawn from an association based on how the data were collected (e.g., the design of the study). 11. Recognize that words such as “normal," “random” and “correlation” have specific meanings in statistics that may differ from common usage. 12. Recognize when to call for help from a statistician. 13. Understand how to obtain or generate data. 14. Understand how to graph the data as a first step in analyzing data and how to know when that’s enough to answer the question of interest. 15. Understand how to interpret numerical summaries and graphical displays of data. 16. Understand how to make appropriate use of statistical inference. 17. Understand how to communicate the results of a statistical analysis. 18. Understand how to interpret statistical results in context. 19. Understand how to critique news stories and journal articles that include statistical information, including identifying what’s missing in the presentation and the flaws in the studies or methods used to generate the information. 20. Understand the concept of a sampling distribution and how it applies to making statistical inferences based on samples of data, including the idea of standard error. 21. Understand the concept of statistical significance, including significance levels and p-values. 22. Understand the concept of confidence interval, including the interpretation of confidence level and margin of error.

Aligned Outcomes