Essay: Common Pitfalls in Experimental Design (original) (raw)

An Overview of Experimental Design

While much research in Biology consists of data collection for descriptive purposes, there is a burgeoning trend toward collecting information with the hope of answering particular questions or to recast information collected for descriptive purposes in light of particular hypotheses. This is due to a number of causes, among them; the growing body of descriptive information on all aspects of natural history and the inappropriateness of existing data sets to answer specific questions. It may also represent an increase in the awareness of scientists of the logical structure of Scientific Method. Whatever the cause, the effect is that biologists are asking questions and designing their research efforts to answer questions. Hence, our concern with asking answerable questions and for developing procedures upon which to base probabilistic inferences regarding the answers to these questions. However, before we discuss particular procedures and their application, some discussion of the form of the questions we ask and the possible outcomes of our attempts to answer a particular question is warranted.

Some Basic Experimental Design Concepts

2008

Experimental design is concerned with the skillful interrogation of nature. Unfortunately, nature is reluctant to reveal her secrets. Joan Fisher Box (1978) observed in her autobiography of her father, Ronald A. Fisher, “Far from behaving consistently, however, Nature appears vacillating, coy, and ambiguous in her answers ” (p. 140). Her most effective tool for confusing researchers is variability—in particular, variability among participants or experimental units. But two can play the variability game. By comparing the variability among participants treated differently to the variability among participants treated alike, researchers can make informed choices between competing hypotheses in science and technology. We must never underestimate nature—she is a formidable foe. Carefully designed and executed experiments are required to learn her secrets. An experimental design is a plan for assigning participants to experimental conditions and the statistical analysis associated with th...

The generation of the experimental design

Internatonal Journal of Scientific Research Updates

Texts and teaching of Experimental Statistics emphasize the statistical analysis of experiments and make only references to the conceptual foundation of experimental research. Basic concepts are vaguely and incompletely defined, and experimental designs are presented as a set of recipes from which one must be chosen for each particular experiment. Consequently, inferences derived from the experiment are often biased. A conceptual basis for the experimental research is considered and a rational procedure for generating the experimental design is suggested. The generation of the experimental design is based on the separate definitions of the structures of the experimental factors and the unit factors, and the association of these two structures by randomization and presence of the experimental factors in the sample. This approach leads to the clear identification of the confounding of the effects from these two structures and of the experimental errors that affect the effects of the experimental factors.

Design of Experiments Application, Concepts, Examples: State of the Art

Design of Experiments (DOE) is statistical tool deployed in various types of system, process and product design, development and optimization. It is multipurpose tool that can be used in various situations such as design for comparisons, variable screening, transfer function identification, optimization and robust design. This paper explores historical aspects of DOE and provides state of the art of its application, guides researchers how to conceptualize, plan and conduct experiments, and how to analyze and interpret data including examples. In addition, this paper reveals that in past 20 years application of DOE have been growing rapidly in manufacturing as well as non-manufacturing industries. It was most popular tool in scientific areas of medicine, engineering, biochemistry, physics, computer science and counts about 50% of its applications compared to all other scientific areas.