57 citations · 97 across the 16 of their papers we have counts for
17 papers
MAD-FC: A Fold Change Visualization with Readability, Proportionality, and Symmetry
Bruce A. Corliss, Yaotian Wang, Francis P. Driscoll +2
We propose a fold change visualization that demonstrates a combination of properties from log and linear plots of fold change. A useful fold change visualization can exhibit: (1) r…
Contra-Analysis for Determining Negligible Effect Size in Scientific Research
Bruce A. Corliss, Yaotian Wang, Heman Shakeri +1
Scientific experiments study interventions that show evidence of an effect size that is meaningfully large, negligibly small, or inconclusively broad. Previously, we proposed contr…
Contra-Analysis: Prioritizing Meaningful Effect Size in Scientific Research
Bruce A. Corliss, Yaotian Wang, Heman Shakeri +1
At every phase of scientific research, scientists must decide how to allocate limited resources to pursue the research inquiries with the greatest potential. This prioritization di…
Playing catch-up in building an open research commons
Philip E. Bourne, Vivien Bonazzi, Amy Brand +12
On August 2, 2021 a group of concerned scientists and US funding agency and federal government officials met for an informal discussion to explore the value and need for a well-coo…
The Least Difference in Means: A Statistic for Effect Size Strength and Practical Significance
Bruce A. Corliss, Yaotian Wang, Heman Shakeri +1
With limited resources, scientific inquiries must be prioritized for further study, funding, and translation based on their practical significance: whether the effect size is large…
The Most Difference in Means: A Statistic for the Strength of Null and Near-Zero Results
Bruce A. Corliss, Taylor R. Brown, Tingting Zhang +3
Statistical insignificance does not suggest the absence of effect, yet scientists must often use null results as evidence of negligible (near-zero) effect size to falsify scientifi…