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

• Autor(a) principal: Pereira, Vanderley José
• Data de Publicação: 2018
• Outros Autores: Asmar, Simone Abreu, Biase, Nádia Giaretta, Luz, José Magno Queiroz, Melo, Berildo de
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Bioscience journal (Online)
• Texto Completo: https://seer.ufu.br/index.php/biosciencejournal/article/view/38778
• topic: Experimental designs; Plant biotechnology; In vitro experimental planning; Agricultural Sciences

Statistical analyses are an essential part of scientific research. Various procedures since the preparation of the experiment have an impact on the statistical procedures adopted. Therefore, a correct planning implies a precise analysis. Through sampling which used a reliability interval at 95% and error margin at 7%, this study aimed to characterize the statistics used by researchers in the field of plant tissue culture, and based on the results, discuss the main impacts of their misuse. Information regarding sample size; number of repetitions; delineation; scheme adopted (factorial or otherwise) and number of treatments; use (or lack of) data transformation; type of the variable (quantitative or qualitative), test and regression types was quantified. Even with consistent use of statistics in plant tissue culture experiments some hindrances remain, such as the size of the portions and the reduced number of repetitions. Added to this, the transformation of data in which criteria for adoption is not reported or informed, or use of ill-informed criteria. Even considering homogeneous conditions, neglecting to use blocking in experiments may misguide researchers. Blocking is recommended to increase the size of the sample, with time as blocking factor, or the human factor involved in the installation of the experiment. The use of factorial designs has been characteristic of the area, which aims to define doses and plant regulators, so the experiments present large number of treatments. For comparison, Tukey's is used for qualitative data, and for the quantitative, and quadratic linear models are preferred.