Confident use of JASP - You will learn how to install JASP, navigate its interface, and run analytical procedures on your own, so you can start working with real datasets faster and with less hesitation.
Clear understanding of the package - You will see what kinds of analyses JASP supports, how its open-source model matters in practice, and when this tool is a good fit for your regular statistical work.
Reliable descriptive statistics - You will practice calculating mode, mean, median, and key dispersion measures, so you can describe datasets correctly before moving on to more advanced statistical analysis.
Better reading of variability - You will learn how to interpret average deviation, standard deviation, and the coefficient of variation, helping you assess how strongly results differ within your dataset.
Choosing the right method - You will understand when to use the t-test, ANOVA, ANCOVA, and regression, making it easier to match a statistical method to your research question and data structure.
Using both statistical approaches - You will work with both frequentist and Bayesian inference in JASP, which will help you compare approaches directly and interpret your analytical results more thoughtfully.
Clear presentation of results - You will learn how to visualize data and analysis outputs in JASP, so you can prepare clearer charts and summaries for reports, presentations, and research publications.
Analyzing relationships - You will master Pearson correlation and linear regression, allowing you to examine the strength of relationships, make predictions, and comment on links between variables.
JASP serves as the starting point for the training by positioning the package in the broader landscape of statistical tools. Attention is given to its main properties, the implications of its open source model, and the kinds of analyses most commonly performed in this environment.
Working effectively with the package requires more than installation alone. The section covers setup, orientation in the graphical interface, and the way analytical procedures are executed in JASP, so users can move smoothly from launching the software to performing core statistical operations.
A key capability of JASP is combining different statistical paradigms within one environment. This section covers frequentist inference, Bayesian inference, and result updating, so participants can understand how analyses are recalculated and how the software supports iterative analytical work.
Basic descriptive analysis starts with identifying a representative value for the dataset. Participants work with mode, mean, and median in JASP, learning how these measures differ, when each is informative, and how the distribution of data affects their interpretation in practice.
Measures of dispersion describe how strongly observations vary around a central value and whether differences in a dataset are substantial or limited. The section works with mean deviation, standard deviation, and the classical coefficient of variation, showing how each supports interpretation of variability.
From comparing means to modeling group effects, the covered methods show how statistical questions are matched with appropriate procedures. The section brings together Student’s t-test, regression, ANOVA, and ANCOVA, with attention to the type of problem each method is designed to address.
When the goal is to quantify the strength and direction of a linear relationship, Pearson’s correlation coefficient becomes the key measure. The section explains how to compute and interpret it in JASP, how to read the sign and magnitude, and how correlation differs from causal inference.
Linear regression is used here to examine how a dependent variable changes in relation to one or more predictors. The section addresses model estimation in JASP, interpretation of coefficients and fit, and the role of regression as a tool for explanation and prediction rather than simple association.
Clear presentation of results is essential when moving from numerical output to interpretation. This part focuses on visualizing data in JASP, selecting suitable charts for variables and comparisons, and using graphics to reveal distributions, relationships, and patterns relevant to statistical reporting.
The closing part consolidates the analytical path covered during the training, from basic descriptive statistics to inference and modeling in JASP. It also helps organize when to use specific procedures, how to read output correctly, and how the discussed methods connect in practical data analysis.
Basic statistics - You should understand core statistical concepts such as variables, measurement scales, and the mean, so you can follow the analyses and interpret the results with confidence.
Working with data - You should be able to work with data tables, identify rows and columns, and understand the structure of a simple dataset, because the training is built around data analysis.
Computer skills - You should be comfortable using a computer, installing software, and navigating application interfaces, so you can complete the exercises in JASP without difficulty.
Reading charts - You should understand basic statistical charts and tables, so you can follow data visualizations more easily and correctly read the results presented during the training.
