Glossary, Cheat Sheets & Reference

Fast, reliable definitions and computation shortcuts for anyone who works with data—helping teams speak the same language, choose the right tests, and compute with confidence.

What this resource is for

This collection gathers concise glossary entries, statistical-test quick references, uncertainty and confidence notes, and common analytic formulas so you can stop hunting across documents when a decision or analysis starts. Use it to onboard new hires, standardize reports, resolve terminology disagreements, and speed routine calculations in meetings, huddles, and day-to-day work.

Who benefits

Designed for analysts, product and operations managers, small-business owners, frontline supervisors, educators, researchers, and anyone who needs practical data literacy: examples include a restaurant manager checking weekly KPIs, a nonprofit measuring program impact, a plant supervisor verifying production metrics, or a researcher choosing the right test for an experiment.

What you'll understand and be able to do

- Find consistent, plain-English definitions for core analytics terms (e.g., bias, variance, KPI, cohort).
- Match common statistical tests to typical questions and data types using the quick-reference cards.
- Use ready formulas for rates, conversions, confidence intervals, and common transformations.
- Recognize when a quick reference is appropriate and when deeper analysis or expert review is needed.

Practical examples

- A service business owner compares two training approaches and uses the statistical-tests quick reference to identify suitable methods for small samples.
- A manufacturing supervisor uses the formula cards to compute defect rates and normalize them for shift-level comparisons.
- An educator standardizes assessment vocabulary across instructors by adopting shared glossary entries to reduce grading inconsistencies.

How this fits inside Data, Analytics & Decision Making

This resource supports the domain goal of moving teams from “what happened?” to “what should we do next?” by removing small, repetitive frictions—misunderstood terms, mismatched tests, and inconsistent formulas—that slow decision-making and introduce avoidable errors. Treat it as a practical foundation for deeper resources such as dashboard design, forecasting, root-cause analysis, and experimentation guidance.

Boundaries and safe use

Cheat sheets speed common tasks but are not a substitute for method training or professional review. Always check data quality and test assumptions before applying a statistical method. If your organization needs enterprise-grade definitions, convert these items into your own master-data glossary and version them through your team’s domain copy.