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With a focus on the generic design phase of the process, this text shows how the methodology for this part of the experiment design process is problem-independent and can thus be applied to experiments conducted in most branches of science and technology.

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Early in my career I was given the task of designing a sub-critical nuclear reactor facility that was to be used to perform basic research in the area of reactor physics. We planned to run a series of experiments to determine fundamental parameters related to the distribution of neutrons in such s- tems. I felt that it was extremely important to understand how the design would impact upon the accuracy of our results and as a result of this - quirement I developed a design methodology that I subsequently called prediction analysis. After working with this method for several years and applying it to a variety of different experiments, I wrote a book on the subject. Not surprisingly, it was entitled Prediction Analysis and was p- lished by Van Nostrand in 1967. Since the book was published over 40 years ago science and technology have undergone massive changes due to the computer revolution. Not - ly has available computing power increased by many orders of magnitude, easily available and easy to use software has become almost ubiquitous. In the 1960´s my emphasis was on the development of equations, tables and graphs to help researchers design experiments based upon some we- known mathematical models. When I reconsider this work in the light of today´s world, the emphasis should shift towards applying current techn- ogy to facilitate the design process.

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Statistical Background.- The Method of Least Squares.- Prediction Analysis.- Separation Experiments.- Initial Value Experiments.- Random Distributions.

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