One-Sample T-Tests with Jamovi: Practical Statistical Analysis

Evaluating whether a sample mean differs significantly from a known benchmark is a core requirement across scientific research and data analysis. This text-based course guides you through conducting, interpreting, and communicating one-sample t-tests using the intuitive, open-source Jamovi platform. You will develop a solid understanding of statistical reasoning, moving from theoretical basics to evaluating real-world sample data and documenting meaningful conclusions. What you'll learn: - Understand essential concepts of hypothesis testing, significance levels, and p-values - Prepare and inspect continuous datasets within the Jamovi interface - Check distributional assumptions using descriptive metrics and normality tests - Execute one-sample t-tests and compute standard effect sizes such as Cohen's d - Interpret confidence intervals, test statistics, and output tables accurately - Apply clear reporting conventions to communicate statistical outcomes effectively Starting with key definitions and statistical foundations, the text systematically progresses through practical analysis workflows and output interpretation. This course is created for beginners, students, and professionals new to statistical analysis and Jamovi, requiring no previous background in statistics. Begin reading to build your data analysis and hypothesis testing skills with Jamovi.