Statistical methods suitable for the analysis of plant tissue culture data

Statistical analyses are an essential part of biological research. Statistical methods are available to biological researchers that range from very simple to extremely complex. Therefore, caution should be used when selecting a statistical method. When possible it is best to avoid complicated statistical procedures that are difficult to interpret and may hinder the researcher's ability to make treatment comparisons. Instead a method should be chosen that compliments a logical and practical treatment design. Statistics should be used as a tool to compare treatments of interest and should not dictate the treatments. Experimental designs should take into account the eventual analysis, otherwise one could conceive of a design that could not be analyzed or, when analyzed, would not answer the desired questions. Therefore, time should be spent before conducting an experiment to plan an experimental design and analysis that best compliments the treatment scheme and questions to be answered. The purpose of this paper is to present examples of experimental designs, means separation procedures, data transformations and presentation methods suitable for plant cell and tissue culture data.Abbreviations ANOVA analysis of variance - BA benzyladenine - CV coefficient of variation - DF degrees of freedom - IAA indole-3-acetic acid - IBA indole-3-butyric acid - LOF lack-of-fit - MSE mean square error - P-ITB phenyl indole-3-thiolobutyrate - S standard deviation - SE standard error of the mean - TDZ thidiazuron