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This research evaluated the importance of a preliminary general analysis of variance (ANOVA) in the interpretation of data from factorial experiments under total nullity. For this, we evaluated the familywise type I error rate (accumulated FWER) of the F test for the unfolding of factorial ANOVA and factorial ANOVA on ranks, which were compared with the FWER for the global effect of treatments. In addition, we evaluated the FWER of the Tukey’s test under total nullity for factorial experiments in the presence or absence of preliminary ANOVA protection (omnibus F test). The study was conducted by simulating data from 2,000 experiments, which were separated into four representative agricultural research scenarios. For both the parametric factorial ANOVA and the non-parametric factorial ANOVA, the FWER significantly exceeded the nominal level of 5%, even under total nullity. While the tests that control the total FWER in the factorials are not being used, the factorial ANOVA should not be performed without the preliminary ANOVA F test showing a significant effect. This, of course, does not apply to tests that are not multiple comparisons, such as Bonferroni, Dunn-Sidak and others, which do not need ANOVA protection. The same recommendation applies to the factorial ANOVA on ranks. [ABSTRACT FROM AUTHOR]

Experimentation is an important methodological basis for innovations in the agricultural sector. Nevertheless, several aspects can be improved in the classical statistical analysis used in agricultural research. The objective of this review was to discuss a few conceptual elements and research results about the validity of statistical tests usually applied in experimentation and present some recommendations that can improve the quality of the analyzes commonly used in the scope of fixed models. Useful elements for the discussion of tests of means, assessment of the condition of normality, and non-parametric analysis options are presented. Understanding the statistical hypotheses and Type I error subtypes, for example, can help in better result interpretation and choice of means test. Some doubts about the evaluation of the normality requirement of the residues explored here can also help researchers better use parametric statistical tools. Finally, we present a general decision flowchart and a brief exemplified discussion of some non-parametric analysis options with emphasis on the differences between classical methods and methods based on generalized models. [ABSTRACT FROM AUTHOR]

Experimental statistics are a key element for innovation in the agricultural sector. Commonly used statistical methods in experimentation are relatively simple, reliable, and widely used. However, the many problems in the quality of statistical analyses reported in the agricultural science literature highlight a need for continuing discussion on and updating of this topic. This article reviews critical points about classic linear models procedures commonly used in agricultural statistics, frequent procedures in publications in the agricultural sciences. Due to the evolution of statistical science some common recommendations from the past should no longer be followed. [ABSTRACT FROM AUTHOR]

The present study aimed to evaluate the ability of a mixture of gabbro powder and dacite powder to promote plant growth, provide nutrients and alter soil chemical characteristics. Two experiments were conducted under different field conditions, in which black oat and beans were added with increasing doses (0 to 10 t ha-1) of the powder rock blend. The grain yield (beans), dry matter production (oats) and the nutritional status of the crops were evaluated. At the end of the experiments, soil samples were collected and subjected to chemical analysis. The application of the rocks resulted in an increase in bean productivity corresponding to 440 kg ha-1 at the dose of 7 t ha-1 in relation to the control. The application of the blend also resulted in an overall improvement in most commonly fertility parameters used, although this improvement was more evident only for phosphorus, copper, and zinc nutrients. In addition, the application of the rock mixture resulted in an increase in the foliar contents of potassium, zinc, copper, and manganese nutrients. Therefore, the material obtained by the mixture of gabbro and dacite, in the proportion of mixture tested, can be considered a multi-nutrient source with evident agronomic viability. [ABSTRACT FROM AUTHOR]

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