Theoretical Fundamentals
Understand the statistical and methodological foundation that makes Work Sampling a scientifically valid technique for industrial engineering.
Why Do Fundamentals Matter?
Work Sampling is not just "counting what workers do." It is an inferential statistical technique based on probability theory, developed by L.H.C. Tippett in 1935. Understanding its fundamentals allows you to:
- Design statistically valid studies
- Defend your results with scientific rigor
- Avoid methodological errors that invalidate your data
- Accurately calculate how many observations you need
Core Topics
Statistical Foundation
Binomial distribution, normal approximation, standard error and sample size formulas.
- โข Sample size formula N
- โข Confidence level Z
- โข Standard error ฯp
- โข Absolute vs relative precision
Sampling vs Time Study
Fundamental differences between Work Sampling and traditional time study.
- โข Instantaneous vs continuous observation
- โข Frequency vs duration
- โข When to use each technique
- โข Advantages and limitations
Activity Taxonomy
Hierarchical classification of tasks following Lean methodology and added value.
- โข VA / NVA / NVA-Necessary
- โข Hierarchical structures
- โข Root causes of delays
- โข MECE (Mutually Exclusive, Collectively Exhaustive)
"Work Sampling is, in essence, a statistical experiment. Any tool that ignores variance, standard error and confidence intervals is, by definition, inadequate for the task."
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