Watch the video below to find out how you can use CycDesigN to speed up your design process and increase the scientific validity of your results.
See the rewards of CycDesign for optimal experimental designs
CycDesigN will generate optimal or near optimal experimental designs for your trials. Whether you are working with field, phenotypic platform, laboratory, clinical or taste testing trials, CycDesigN experimental design software supports gains in the efficiency and effectiveness of your tests, which can reduce costs and improve the outcomes of your testing trials.
CycDesigN produces an extensive range of statistically robust experimental designs that are quick and easy to generate and practical to implement. CycDesigN generates the best possible experimental designs with one or two-dimensional blocking structures for a range of design parameters and design types.
The software includes options to produce resolvable and non-resolvable IB and row-column designs, alpha-design, along with many more options. Optimal spatial design can be generated by CycDesigN and considers the separations between treatments. This is done using an autoregressive correlation phenomenon that acknowledges that correlation reduces as experimental units get further apart.
Once your optimised experimental design has been constructed, the output generated includes Genstat or SAS code as well as various file options to view and implement the design.
Exploring CycDesign experimental design software for your sector
CycDesigN offers 4 core design types
Resolvable designs have the treatments arranged in groups of blocks or rows and columns such that each treatment occurs once in each set. They can be latinized, t-latinized or partially latinized.
Non-resolvable designs are available for situations where the design cannot be resolved into replicate groups.
P-rep designs offer 2 variations. Single location p-rep designs are an alternative to unreplicated designs. Several standard treatments (controls) are typically included for comparative purposes and to contribute to an estimate of error. Multi-location p-rep designs have test treatments appearing either once, twice or not at all in each location. Each treatment occurs as equally as possible in the overall design.
Crossover designs are used when sequences of treatments are applied to several subjects over a number of time periods.