Statistics applied to plant micropropagation: A critical review of inadequate use

Abstract

Statistical analyses are an essential part of scientific research. Several choices, including the setup of the experiment, influence the selection of statistical procedures. Thus, successful planning implies accurate analysis. We used a 95% confidence interval and a 7% error margin to sample and characterize the statistical techniques used in studies on micropropagation and to discuss the effects of misusing these techniques. We quantified the following: sample size, number of replications, design, scheme (factorial or not factorial) and number of treatments, whether data transformation was used, transformation type and criteria for selection; variable type (quantitative or qualitative); statistical test and regression types. Although statistics were consistently used in these micropropagation experiments, there were several limitations such as small plot sizes, low replication numbers, employing data transformation while neglecting to inform the criteria used or even using the wrong criteria. Although statistical approaches were applied homogeneously, neglecting to use blocking can lead to errors. Blocking is recommended to increase sample size. For example, the times of an experiment or the number of people needed to set up an experiment can be used as blocks. Micropropagation studies typically employ factorial experiments to identify plant regulator types and application rates. Thus, these experiments have numerous treatments. The Tukey test is used for qualitative data while regression models (linear and quadratic) are more frequently used for quantitative data.