In this module the students will be exposed to in-depth understanding of different research methodologies and a difference between qualitative and quantitative method of data collection. Gradually they will develop the skills to analyze and critically evaluate empirical studies across various domains of psychology, including cognitive, social, clinical, and neuroscience research.

The present module provides an in-depth understanding of the Normal Probability Curve (NPC) — one of the most fundamental concepts in the field of psychological statistics. Through this module, students will explore the theoretical foundations, mathematical properties, and practical applications of the normal distribution in psychological measurement, assessment, and research. Emphasis will be placed on how the normal curve helps psychologists interpret test scores, identify individual differences, and make probabilistic decisions about behavior and cognition.

This module introduces students to the fundamental concepts and techniques of Descriptive Statistics of data analysis in psychology. Descriptive statistics allow psychologists to summarize, organize, and interpret large sets of behavioral data. The module emphasizes the importance of descriptive statistics in explaining quantitative data.

This module offers an in-depth exploration of Inferential Statistics, the branch of statistical that allows psychologists to draw conclusions about populations based on sample data. While descriptive statistics summarize observed data, inferential statistics enable researchers to make valid generalizations, test hypotheses, and estimate relationships among psychological variables. In this module, students will learn how inferential methods bridge the gap between raw data and theoretical interpretation — forming the foundation of evidence-based psychological research.

This module introduces the concept of correlation as a statistical tool for understanding the relationship between two variables in psychological research. Students will learn to identify positive, negative, and zero correlations and interpret the strength of these relationships. The course distinguishes between Pearson’s and Spearman’s correlations and explains when each method should be used. Emphasis is placed on avoiding the “correlation = causation” fallacy and recognizing the role of third variables. Students will also examine spurious correlations and practice interpreting scatterplots. The module develops foundational skills for analyzing behavioral, cognitive, and clinical data.

This module provides an in-depth understanding of parametric and non-parametric statistical methods and their crucial roles in psychological research. Students will learn how different statistical tests are chosen based on data type, distribution, and research design. The module emphasizes the importance of assumption testing, selection of appropriate statistical procedures, and accurate interpretation of results in behavioral, clinical, and cognitive psychology contexts.

This module introduces one of the most fundamental inferential statistical tools used in psychological research — the t-test. It explores how this test allows researchers to determine whether differences between groups or conditions are statistically significant or likely due to chance. The focus is on understanding when and how to apply different types of t-tests, their assumptions, computation, and interpretation within psychological Variables such as attention span, digital span and treatment effectiveness. Through both conceptual learning and practical exercises, students will learn to interpret t-test results, and report findings in APA format.

This module provides a detailed exploration of One-Way Analysis of Variance (ANOVA) - a fundamental inferential statistical technique used to compare the means of three or more groups within psychological research. Highlighting the limitation of t-tests, this module guides students to understand how ANOVA identifies whether observed group differences are statistically significant or due to random variability. Students will understand the logic behind different types of variances, underlying assumptions of ANOVA, and how to apply the technique to real psychological data along with interpreting results, reporting findings in APA format.

This module introduces students to Repeated Measures Analysis of Variance (ANOVA) - a powerful statistical technique used when the same participants are measured under multiple conditions or over time. In psychological research, this method is especially valuable for studying cognitive performance, learning, emotion, or treatment effects across repeated sessions. This module will also introduces students to Two-Way Analysis of Variance (ANOVA), an advanced extension of the One-Way ANOVA — that allows researchers to examine the main effects of two independent variables and their interaction on a dependent variable.

This module introduces students to the principles and applications of Regression Analysis, one of the most widely used statistical methods in psychological research. Regression allows psychologists to quantify and predict relationships between variables, such as how hours of sleep (IV) influence Digital Span Task (DV). Students will learn the conceptual and numerical foundation of linear regression, understanding of multiple regression and the interpretation of slope and interception, and how to assess model fit.

This module introduces students to the principles and applications of Non-Parametric Statistics, focusing on statistical techniques that do not assume a normal distribution of data. Unlike parametric tests, non-parametric methods are ideal for small samples, ordinal data, and non-normally distributed psychological measures.