STAT 503 Lesson 1 - Introduction to Design of Experiments

Comprehensive vocabulary terms and definitions from STAT 503 Lesson 1, covering the scientific method, DOE history, core principles (randomization, replication, blocking), factor types, and experimental planning.

STAT 503 Focus

The course focuses primarily on experimental design rather than statistical analysis; it is described as more conceptual than math-oriented.

Scientific method - first step

Decide what phenomenon you wish to investigate.

Cause-and-effect conclusion

A conclusion supported when changing a factor causes the phenomenon/response to change under controlled conditions.

Comparative experiment

An experiment that compares conditions, such as a treatment group and a control group.

Treatment-control factor structure

A setup where a treatment group and control group represent one factor with two levels.

Robustness (Statistical Analysis)

A quality of a technique that is not overly influenced by bad data or outliers and still produces an appropriate answer.

Process robustness

The idea that a process should continue to work despite variation in who or what is involved.

General process model

An experimental model consisting of inputs, controllable factors, uncontrollable factors, and an output/response.

Controllable factors

Experimental factors whose settings can be specified and controlled, such as baking time or temperature.

Uncontrollable factors

Factors affecting the outcome that the experimenter cannot directly control.

R. A. Fisher

A major early figure in DOE whose work in agricultural science established the foundations of modern experimental design, including ANOVA and factorial designs.

Frank Yates

A colleague of R. A. Fisher who helped develop many concepts and procedures used in experimental design.

Sequential analysis

A statistical method developed during World War II, notably used to improve the accuracy of long-range artillery guns.

Box and Wilson

Key figures who published a key 1951 paper on response-surface methodology and industrial applications.

George Box

An important statistician in response-surface methodology who worked in the chemical industry and married R. A. Fisher's daughter.

Quality revolution

Another name for the second industrial era (late 1970s-1990), emphasizing statistical quality control and experimental design.

CQI

Continuous Quality Improvement; a management goal focused on ongoing process enhancement.

TQM

Total Quality Management; a management technique associated with the statistical quality revolution.

W. Edwards Deming

The statistician who brought the importance of statistical quality control to Japan in the 1950s.

Taguchi

A Japanese engineer associated with orthogonal arrays, robust parameter design, and process robustness.

Orthogonal arrays

Experimental-design structures developed by Taguchi that are similar to Western fractional factorial designs.

Robust parameter design

A Taguchi-associated approach focused on choosing parameter settings that make processes or products robust.

Six Sigma

A quality-oriented approach popular since 1990 that uses statistics, quality measures, and feedback loops to guide decisions; viewed as a newer form of CQI.

Randomized double-blind clinical trial

The gold standard for the approval of new medical products, designed to eliminate bias inherent in anecdotal studies.

Randomization

The assignment of treatments to experimental units using a random process to eliminate potential bias.

Experimental unit

The unit to which a treatment is assigned in an experiment.

Replication

Repeating observations or treatment applications across experimental units to estimate uncertainty and improve precision.

Standard error of the mean

The standard deviation of the sampling distribution of the sample mean, defined as the square root of the estimated variance of the sample mean.

Standard-error formula

$SE(\bar{x}) = \frac{s}{\sqrt{n}}$ , equivalently $\sqrt{\frac{s^{2}}{n}}$, under independent-sample settings.

Blocking

A design technique that incorporates factors responsible for undesirable variation so their contribution can be accounted for and error variance reduced.

Nuisance factor

A factor that is not the primary scientific interest but contributes variability that must be addressed (often through blocking).

Treatment factor

A factor of primary scientific interest in an experiment.

Multi-factor design

An experimental design that studies combinations of multiple factors simultaneously rather than one at a time.

Interactions

Relationships in which the effect of one factor depends on the level of another factor.

Confounding

A situation where the effects of two factors are mixed together so their separate contributions cannot be distinguished.

Planning step 1

Recognition and statement of the problem.

Experimental factor

A factor whose levels can be specified/set by the experimenter and randomly assigned as treatments.

Classification factor

A characteristic that cannot be changed or randomly assigned, such as age or sex, acting as an inherent attribute of the experimental unit.

Quantitative factor

A factor for which specified numerical levels can be assigned, such as concentration or pH level.

Qualitative factor

A categorical factor consisting of different types, such as plant species or brand, rather than a numerical scale.

Sequential experimentation

An iterative process where current knowledge informs the design of the next experiment.