Learning Block Performance and Outlier Analysis

Group median values and statistical outliers are identified for a final learning block and a pre-sleep test. Data is provided in a 16.7 KB XLSX file by author Xianhui He, last updated in April 2026. Outliers are flagged using a three-scaled median absolute deviation (MAD) method from the group median.

Use Cases

• Analyze the relationship between group median performance in the final learning block and the pre-sleep test.
• Identify and study participant outliers flagged for exceeding three-scaled MAD from the group median.
• Compare the distribution of performance metrics between the two recorded phases (learning block and pre-sleep test).

Strengths

• Dataset is openly licensed under CC-BY-4.0.
• Provides a clear statistical method (three-scaled MAD) for outlier identification.

Limitations

• Extremely small dataset at only 16.7 KB, indicating very limited sample size.
• No column names, row counts, or sample data are provided for assessment.

Provenance

Source

Xianhui He via figshare.

Freshness

Last updated April 2026.