Campus Safety (Clery Act) Compliance Mapper
Audits Annual Security Reports (ASR) for Clery Act regulatory alignment and disclosure gaps.
Statistics prompt templates.
Audits Annual Security Reports (ASR) for Clery Act regulatory alignment and disclosure gaps.
Designs a methodology to measure what would have happened without the intervention.
Determines optimal safety stock levels using demand variance and lead time uncertainty.
Detect and correct for cognitive biases (anchoring, halo effect, overconfidence) in subjective expert estimates.
Establishes a statistically sound cycle counting program to replace annual physical counts.
Technical decision tree for operators when control charts signal a process shift.
Outlines the primary and secondary statistical methods, populations for analysis, and handling of missing data.
Standardizes raw study results into a uniform format suitable for meta-analysis or systematic review.
Provides a standardized critical appraisal of a medical research paper for peer-review discussion.
Check that the chosen test matches the data's measurement scale and dependence structure before any p-value is generated.
Interrogate a statistical result for power, confounding and overclaiming.
Pre-commit to a correction procedure so a wide exploratory sweep does not produce a selective significance narrative.
Convert an 'no effect found' conclusion into a statement about what effect sizes remain compatible with the data.
Make the full conditioning set behind a p-value explicit so readers cannot silently extrapolate it.
Assess agreement between studies by estimating the difference in effects rather than by sorting p-values around a threshold.
Catch and rewrite p-value statements that assert hypothesis probabilities, proof, or absence of effect.
Replace probability-of-containment and overlap-based reasoning with compatibility and width-based reasoning.
Match an available data structure to the identification strategy that removes the specific selection problem present.
Compare an outcome across segments using grouped statistics, contingency tables and derived binary features before any modelling.
Attribute an eye-catching metric correlation between a genuine effect and self-selection before anyone builds a roadmap on it.