The aim of this course is to convey fundamental and inferential statistical methods to students at a theoretical level and to equip them with the skills to apply these methods to agricultural and economic data. The course aims to develop students' competencies in summarizing data using graphs and tables, estimating population parameters, selecting appropriate parametric (t-tests, ANOVA) or non-parametric hypothesis tests based on data structure, constructing correlation and regression models, and analyzing agricultural experimental designs (randomized blocks) to report results in accordance with scientific and professional standards.

This course covers the fundamental and inferential statistical methods required for research in agricultural, economic, and social sciences. Topics to be addressed include data collection, organization, and summarization via graphs and frequency tables, as well as measures of central tendency and dispersion. Within the framework of hypothesis testing principles, the course will provide practical instruction on parametric tests (t-tests, One-Way Analysis of Variance - ANOVA) suitable for specific data structures, non-parametric alternatives, and Chi-Square analysis. Essential statistical analyses required for agricultural engineering research will be examined through both theoretical study and computer-aided applications.