ScottKnottESD implements a mean comparison approach using hierarchical clustering to partition treatment means into statistically distinct groups. The method is described in a 2018 paper by Chakkrit Tantithamthavorn, published in IEEE Transactions on Software Engineering. The dataset likely contains results or parameters for applying this statistical test to fields like model evaluation or feature importance analysis.
Use Cases
- Partitioning model performance means into statistically distinct groups based on the described hierarchical clustering.
- Comparing treatment means, such as variable importance scores, to identify non-negligible differences.
- Applying the Scott-Knott ESD test for post-hoc analysis in experimental studies.
Strengths
- Method is grounded in a peer-reviewed 2018 publication from IEEE Transactions on Software Engineering.
- The description specifies a clear statistical purpose: partitioning means into groups with non-negligible difference.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- Last update date is unknown; freshness unverified.
Provenance
- Source
- Chakkrit Tantithamthavorn
- Collection Method
- Likely contains data or code related to the statistical method described in the referenced paper.
- Time Range
- null
- Freshness
- Last updated is unknown.
- Geography
- null