Work Sampling KPIs for Industrial Dashboards
Present proportions, coverage and uncertainty in a dashboard without treating them as direct productivity measures.
WORKSAMP · LEARNING RESOURCES
Statistical foundations
Sample size, precision, confidence intervals and the limits of inference.
← All guidesWhat do you need to work on?
Statistical foundations
Sample size, precision, confidence intervals and the limits of inference.
Explore guidesStudy design
Observation plans, categories, routes and reliable data capture.
Explore guidesResults and decisions
Proportions, time, capacity and improvement decisions grounded in data.
Explore guidesIndustry applications
Examples and limits in manufacturing, maintenance and services.
Explore guidesIndustrial management
Tools, organization and context for putting a study into practice.
Explore guidesArticles and practical examples
Showing 1–12 of 16
Observation Frequency: Planning Your Daily Rounds
Distribute observations across days and rounds without confusing total records with independent observations.
Absolute Precision vs. Relative Precision: Which to Choose?
Distinguish percentage points from relative error when defining precision for an observed proportion.
Stratified Sampling
Estimate proportions in stratified sampling using appropriate weights and design-consistent uncertainty.
Sample Size and the Central Limit Theorem
What the central limit theorem explains and why a large sample does not correct bias or guarantee independence.
What is a Confidence Interval and why does your Plant need it?
Interpret a confidence interval and communicate uncertainty without assigning probabilities to the fixed parameter.
The Pilot Study: Refining the Sample
Use a pilot to test categories, coverage and study feasibility without assuming automatic statistical guarantees.
P-Charts for Validating Studies
Use a p-chart with defined subgroups, comparable denominators and suitable control limits.
Randomness: The Key to Statistical Validity
Select observation times and understand why randomness, independence and absence of bias are different concepts.
The Standard Error (σp) and Data Precision
Calculate a proportion’s standard error and recognise when dependence or study design changes its interpretation.
Confidence Level: 95% or 99%?
Choose between 95% and 99% confidence while accounting for precision, cost and sampling-design assumptions.
Sample Size Calculation (n): The Essential Formula
Plan observations from confidence, absolute precision and an anticipated proportion, with rounding and model limitations.
Search covers all the guides in this collection, not just the current page.
From a question to a study you can use
Explore the app or ask about a demonstration using your context.
Explore WorkSamp →