Class Statistics II

  • Presentation


    The syllabus for this curricular unit aims to apply univariate and bivariate inferential data analysis techniques to specific problems in the field of psychology. It was developed in accordance with the course curriculum and the program of studies for other curricular units.

  • Code


  • Syllabus


    PC1. The foundations of statistical inference: Introduction to probability theory; 

    PC2. Parameter estimation (punctual and interval) and hypothesis testing;

    PC3. Sample distributions; 

    PC4. Parametric and non-parametric tests: assumptions and conditions of application;

    PC5. Hypothesis testing for one population: Student's t-test for one sample, Wilcoxon signed-rank test; 

    PC6. Pearson and Spearman correlation coefficients and association test: Chi-square of association/independence and adjustment;

    PC7. Hypothesis testing for two populations:Student's t-test for independent samples, Student's t-test for related samples, Mann-Whitney test, Wilcoxon test;

    PC8. Hypothesis testing for three or more independent populations and multivariate analysis: One-way ANOVA test, Kruskall-Wallis test; ANCOVA, MANOVA

    PC9. Hypothesis testing for three or more dependent populations: ANOVA with repeated measures, Friedman test

    Note.  CP = program contents

  • Objectives


    UC has the following learning objectives:

    LO1. Apply fundamental concepts of inferential statistics to the study of one, two or more populations;

    LO2. Calculate and analyze point estimates and intervals of parametric and nonparametric parameters for a population;

    LO3. Calculate and quantify the relationship or association between two variables;

    LO4. Calculate and analyze differences, point estimates, and intervals of parametric and nonparametric parameters between two populations;

    LO5. Calculate and analyze point estimates and intervals of parametric and nonparametric parameters for three or more populations;

    LG6. Assess, select, and apply various statistical procedures while considering the research objectives;

    LO7. Use the JASP® statistical analysis software effectively and proficiently.

    Note. LO = learning outcomes

  • Teaching methodologies and assessment

    Teaching methodologies and assessment

    The teaching-learning methodology employed adopts an active approach by delivering content within a laboratory setting. This methodology emphasizes experimentation and encourages cooperation among students. Additionally, whenever feasible, students are actively involved in ongoing research projects conducted within the Research and Development unit (HEI-Lab). 

  • References


    Coladarci, T., Cobb, C. D., Minium, E. W., & Clarke, R. C. (2011). Fundamentals of Statistical Reasoning in Education. Faculty and Staff Monograph Publications, 57.

    Coolican, H. (2018). Research Methods and Statistics in Psychology (7th Edition). Routledge.

    Field, A. P. (2018). Discovering statistics using IBM SPSS Statistics (5th Edition), Sage.

    Goss-Sampson, M. A. (2022). Statistical Analysis in JASP 0.16.1: A Guide for Students. (

    Howitt, D., & Cramer, D. (2014). Introduction to statistics in psychology (6th Edition), Pearson.

    Navarro et al. (2019). Learning Statistics with JASP: A Tutorial for Psychology Students and Other Beginners.(

    Reinhart, A. (2015). Statistics done wrong: The woefully complete guide, No Starch Press

Lisboa 2020 Portugal 2020 Small Logo EU small Logo PRR republica 150x50 Logo UE Financed Provedor do Estudante Livro de reclamaões Elogios