Statistics

General Foundation

General Foundation

Upon successful completion of the course, students will: • Understand the fundamental principles of statistics. • Be able to organize and summarize data numerically and through graphical representations (types of data and variables, measurement scales, histograms, tables, measures of central tendency and dispersion, frequency distribution description, types of distributions). • Demonstrate the ability to examine the relationship between quantitative data (dot plots, direction and strength of correlation, differences between correlation and causation, linear regression). • Understand the connection between probability and inferential statistics (research and null hypotheses, testing and interpretation of the null hypothesis). • Be able to identify the appropriate statistical test to examine relationships between quantitative data, construct a simple regression equation, and compare two samples using the t-test. • Be familiar with basic statistical analyses using software (descriptive statistics, correlation, regression, comparison of two samples) and interpret the results.

COURSE PURPOSE AND DESCRIPTION The purpose of the course Statistics is to introduce students to the fundamental concepts and techniques of descriptive and inferential statistics, with direct application in physical education and sport science. Students will understand the importance of statistics in analyzing and interpreting research data, become familiar with tools for describing and comparing data, and learn how to apply appropriate statistical tests to draw evidence-based conclusions. The course focuses both on theoretical comprehension and hands-on practice through examples and data analysis using statistical software. COURSE OBJECTIVES The course aims to: - Promote understanding of the basic principles of statistics and their role in scientific research. - Develop skills in data description and summarization using statistical indicators and graphical methods. - Familiarize students with methods for analyzing relationships and regressions of quantitative variables. - Apply inferential statistical techniques for group comparison and data-driven decision-making. - Provide practice in using statistical software and interpreting real-world results. LEARNING OUTCOMES Knowledge - Recognize and understand core statistical concepts, variable types, and statistical measures. - Describe and summarize data using appropriate tools (tables, graphs, measures of central tendency and variability). - Distinguish between various statistical techniques and understand when each is applicable. Skills - Perform statistical analyses such as t-tests, ANOVA, correlation, and regression. - Select and apply appropriate statistical tests depending on data type and research design. - Use statistical software to analyze datasets and accurately interpret the results. Competencies - Combine statistical reasoning with scientific logic to support research conclusions. - Apply statistical knowledge to problems in physical education and sports contexts. - Communicate statistical findings clearly and with proper justification. COURSE CONTENT AND ALIGNMENT WITH LEARNING OUTCOMES 1. Introductory Lecture – Basic Concepts and Definitions Introduction to core statistical terms such as population, sample, parametric and non-parametric methods, variables, and data types. Learning Outcome: Understand the framework and significance of statistics in scientific research and recognize key terms. ⸻ 2. Types of Variables – Measures of Central Tendency and Dispersion Presentation of variable types (qualitative, quantitative) and basic data descriptors (mean, median, mode, standard deviation, variance, range). Learning Outcome: Classify variables and calculate and interpret descriptive statistics. ⸻ 3. Descriptive Statistics In-depth exploration of descriptive statistics techniques, including graphical and numerical data presentation and interpretation. Learning Outcome: Describe and present datasets using appropriate statistical and visual methods. ⸻ 4. Normal Distribution / Hypotheses and Type I & II Errors Analysis of the normal distribution and its properties; introduction to hypothesis testing and types of statistical errors. Learning Outcome: Understand the role of distribution assumptions and statistical errors in decision-making. ⸻ 5. Simple Linear Correlation Presentation of Pearson and Spearman correlation, relationship between two variables, and statistical significance. Learning Outcome: Analyze relationships between variables and interpret correlation coefficients. ⸻ 6. Chi-square (Χ²) Analysis Introduction to Χ² tests for independence and goodness of fit; application to categorical data and contingency tables. Learning Outcome: Apply Χ² tests and evaluate relationships between qualitative variables. ⸻ 7. Paired Samples t-test and Wilcoxon Test Comparison of parametric (t-test) and non-parametric (Wilcoxon) tests for dependent samples. Learning Outcome: Understand when and how to apply tests for related groups. ⸻ 8. Independent Samples t-test and Mann–Whitney Test Parametric and non-parametric methods for comparing two independent groups. Learning Outcome: Assess differences between independent groups using appropriate statistical methods. ⸻ 9. One-way ANOVA and Kruskal–Wallis Test Methods for comparing more than two groups: parametric (ANOVA) and non-parametric (Kruskal–Wallis). Learning Outcome: Identify differences across multiple groups and select suitable analysis methods. ⸻ 10. Two-way ANOVA Analysis of factorial designs studying two independent variables and their interaction effects. Learning Outcome: Interpret results from two-factor experimental designs. ⸻ 11. Two-way Repeated Measures ANOVA and Friedman Test Statistical methods for repeated-measures designs: parametric (repeated ANOVA) and non-parametric (Friedman). Learning Outcome: Recognize appropriate methods for analyzing repeated-measures data. ⸻ 12. Simple Linear Regression Introduction to prediction, cause-effect relationships, and modeling with simple linear regression. Learning Outcome: Use regression analysis for predicting dependent variable outcomes. ⸻ 13. Review Lecture Summary of course content, exam preparation, clarification of concepts, and applied practice. Learning Outcome: Consolidate knowledge, assess concept mastery, and prepare effectively for the final evaluation.

statistics, data analysis, comparison, correlation, description, regression

15-06-2025