Abstract
There are few previous studies that investigate the most used statistical techniques in animal science. Due to the large number of tools and methods available for statistical analysis, it is important to identify the most applied ones for this area of research. Therefore, we aimed to identify the use of different statistical techniques (designs, software and analysis) used in two Brazilian journals (Ciência Rural and Revista Brasileira de Zootecnia) and one international journal (Journal of Animal Science). In order to do this, scientific articles published during the years 2011 to 2015 were selected to form a database. Our article discusses the use of designs, software and analyses most commonly used in the journals studied. To study this, we used descriptive statistics and multivariate approaches. Completely randomized and randomized blocks design were the principal designs used in animal science. The SAS® software was the principal software used. Finally, analysis of variance was the principal statistical method, followed by regression analysis. There were no differences between the journals over time regarding the use of statistical analyses. The results highlight the importance of hypothesis testing within animal science.
Highlights
Scientific experimentation consists in planning, implementing, collecting sample material, performing statistical analysis, interpreting results, and drawing conclusions
The principal findings in this study indicate that CRD and RBD are the principal experimental designs used in animal science
Random design and randomized block design are the principal experimental designs used in animal science
Summary
Scientific experimentation consists in planning, implementing, collecting sample material, performing statistical analysis, interpreting results, and drawing conclusions. Considering statistical analysis, which allows one to describe and interpret results, besides facilitating the understanding of scientific research. The use of statistical analysis is extremely important because there are non-controlled factors related to biological systems, and its standardization is relevant to the correct interpretation of data (FESTING and ALTMAN, 2002). Statistical analysis allows verification if there are differences between the factors studied, as well as the interpretation of such significance. The researcher must know how to differentiate between statistical significance results and biological. There are numerous tests and software that carry out statistical analysis faster and more practically
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