How to choose an appropriate experimental design type to arrange research factors and their levels is an important issue in experimental research. Choosing an appropriate design type is directly related to the accuracy and reliability of the research result. When confronting a practical issue, how can researchers choose the most appropriate design type to arrange the experiment based on research objective and specified situation? This article mainly introduces the related contents of the single-group design and the paired design through practical examples.

The general problems existing in the clinical trials of investigational new drugs involve some key aspects such as the guiding principles, research designs, quality controls and statistical analyses. This paper explores the eight general issues in the clinical trials of investigational new drugs and presents precautionary measures with high operability. Research on the clinical trials of investigational new drugs is a complex project, which should be carried out strictly according to the policies, laws, criteria and operating rules set by related agencies. The neglect of research designs and data analyses will lead clinical trials to failure.

Three-factor designs that are unable to examine the interactions include crossover design and Latin square design, which can examine three factors, namely, an experimental factor and two block factors. Although the two design types are not quite frequently used in practical research, an unexpected research effect will be achieved if they are correctly adopted on appropriate occasions. Due to the limit of space, this article introduces two forms of crossover design.

Three-factor designs that are unable to examine the interactions include crossover design and Latin square design, which can examine three factors: an experimental factor and two block factors. Although the two design types are not quite frequently used in practical research, an unexpected research effect will be achieved if they are correctly adopted on appropriate occasions. This article introduced the 3×3 crossover design and the Latin square design by examples.

Multifactor designs that are able to examine the interactions include factorial design, factorial design with a block factor, repeated measurement design; orthogonal design, split-block design, etc. Among all the above design types that are able to examine the interactions, the factorial design is the most commonly used. It is also called the full-factor experimental design, which means that the levels of all the experimental factors involved in the research are completely combined, and k independent repeated experiments are conducted under each experimental condition. The factorial design with a block factor can also examine the influence of a block factor formed by one or more important non experimental factors based on the factorial design. This article introduces the factorial design and the factorial design with a block factor by examples.

Two-factor designs are quite commonly used in scientific research. If the two factors have interactions, research designs like the factorial design and the orthogonal design can be adopted; however, these designs usually require many experiments. If the two factors have no interaction or the interaction is not statistically significant on result in theory and in specialty, and the measuring error of the experimental data under a certain condition (usually it is one of the experimental conditions which is formed by the complete combination of the levels of two factors) is allowed in specialty, researchers can use random block design without repeated experiments, balanced non-complete random block design without repeated experiments, single factor design with a repeatedly measured factor, two-factor design without repeated experiments and two-factor nested design. This article introduced the first three design types with examples.

Two-factor designs are very commonly used in scientific research. If the two factors have interactions, research designs like the factorial design and the orthogonal design can be adopted; however, these designs usually require many experiments. If the two factors have no interaction or the interaction is not statistically significant on result in theory and in specialty, and the measuring error of experimental data under a certain condition (usually one of the experimental conditions that are formed by the complete combination of the levels of the two factors) is allowed in specialty, researchers can use random block design without repeated experiments, balanced incomplete random block design without repeated experiments, single factor design with a repeatedly measured factor, two-factor design without repeated experiments and two-factor nested design. This article introduces the last two design types by examples.