Basic Statistics and Data Analysis

Lecture notes, MCQS of Statistics

Design of Experiments Overview

Design of Experiments Overview

An experiment is usually a test or trial or series of tests. The objective of the experiment may either be

  1. Confirmation
  2. Exploration

Designing of an experiment means, providing a plan and actual procedure of laying out the experiment. It is a design of any information gathering exercise where variation is present under the full or no control of the experimenter. The experimenter in design of experiments is often interested in the effect of some process or intervention (the treatment) on some objects (the experimental units) such as people, parts of people, groups of people, plants, animals etc. So the design of experiment is an efficient procedure for planning experiments so that the data obtained can be analyzed to yield and object conclusions.

In observational study the researchers observe individuals and measure variables of interest but do not attempt to influence the response variable, while in an experimental study, the researchers deliberately (purposely) impose some treatment on individuals and then observe the response variables. When the goal is to demonstrate cause and effect, experiment is the only source of convincing data.

Statistical Design

By the statistical design of experiments, we refer to the process of planning the experiment, so that the appropriate data will be collected, which may be analyzed by statistical methods resulting in valid and objective conclusions. Thus there are two aspects to any experimental problem:

  1. The design of the experiments
  2. The statistical analysis of the data

There are many experimental design which differ from each other primarily in the way, in which the experimental units are classified, before the application of treatment.

Design of experiment (DOE) helps in

  • Identifying the relationships between cause and effect
  • Provide some understanding of interactions among causative factors
  • Determining the level at which to set the controllable factors in order to optimize reliability
  • Minimizing the experimental error i.e., noise
  • Improving the robustness of the design or process to variation

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