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Food Safety Agricultural Research Product Development Alternative Agriculture Consulting

Statistical Analysis

Pacific Ag provides clients with unbiased interpretation of results using scientifically valid statistical tests to evaluate product effectiveness.

 
weed control research statistical evaluations of agricultural experiments

A comprehensive research report to the client must include statistical data analysis using appropriate mathematical tests for statistical significance at various levels of confidence, which depend on the variability of the sample population. Analysis of variance, f and T tests, non-parametric tests, regression analysis, and many other specific statistical tests for treatment effects are used to determine if the degree of effect is significant .

Pacific Ag scientists are trained in the scientific method and utilize these techniques in every study we conduct on products such as pesticides, biological control agents, seed types, soil amendments, new designs of farm equipment, and almost any new technology tested against conventional methods. These technologies are used in replicated experiments where data is collected and entered into computer databases using the latest versions of analytical software specifically designed for the agricultural research industry.

Our software capabilities include every available software system, including Gylling’s Agricultural Research Manager, Field Pro, Astrix, and American Ag’s eFTN. Our staff is also trained in Microsoft PowerPoint, Excel and Access, for custom graphics and clear presentation of results in a format familiar to and accessible by the client for presentations and transcription into internal company documents.

As a complete statistical evaluation does not stop with mathematical tests for statistical significance, our experimental result reports are full of explanatory graphs and tables that allow for in-depth review of the most complex scientific work by management. Graphs include standard and three-dimensional histograms of multiple experimental variables, regression curves with coefficients of variation data displayed, and visual representations of proportions and other factors represented as pie charts or population curves through time. All of these statistical and graphical representations facilitate understanding of complex experimental results - even the best work is useless unless it is easily understood by the end user.