Detailed coverage of the assumptions underlying parametric and non-parametric statistics, the four scales of measurement, and their implications for choosing appropriate statistical tests.
Understand Biserial and Tetrachoric Correlation — non-Pearson methods used when dichotomous variables have an underlying continuous distribution. Learn when to use them and how they differ from Point Biserial and Phi.
Master bivariate (simple linear) regression — computing slope (b) and intercept (a) using Ordinary Least Squares, constructing the regression equation, and generating predicted values for psychological data.
Understand the concept of correlation in psychology — its meaning, direction (positive/negative/zero), strength, graphical scatter diagrams, and the distinction between linear and curvilinear relationships.
Comprehensive guide to descriptive statistics, data organisation methods, frequency distributions, and tabulation techniques in psychological research.
Master graphical and diagrammatic techniques for presenting psychological data including histograms, frequency polygons, ogives, bar diagrams, and pie charts.
Master inferential statistics: estimation, point and interval estimation, hypothesis testing, null and alternative hypotheses, significance levels, Type I and Type II errors, and statistical power.
Understand the fundamentals of simple linear regression — the regression line, regression equation, slope and intercept, least squares criterion, and how regression relates to correlation in psychological research.
Master the concept of levels of significance (0.05 and 0.01), confidence limits, the meaning of α, and how these form the decision framework for hypothesis testing in psychological research.
Comprehensive guide to measures of central tendency (mean, median, mode), dispersion (range, SD, variance), and distribution shape (skewness, kurtosis) in psychology.
Comprehensive guide to the major non-parametric statistical tests—chi-square, median test, Mann-Whitney U, sign test, and Wilcoxon matched-pairs signed-ranks test—with formulas, applications, and worked examples.
Comprehensive guide to the normal distribution concept, properties of the Normal Probability Curve (NPC), theoretical foundations, and its significance in psychological measurement.
How to use the Table of Areas under the Normal Probability Curve, convert raw scores to z-scores, and read NPC table values for psychological and educational applications.
Master the distinction between one-tailed and two-tailed hypothesis tests, directional vs non-directional hypotheses, and understand how decision errors arise from correct statistical procedures.
Comprehensive guide to One-Way ANOVA: the F-ratio, between/within group variance, assumptions, worked examples with post-hoc t-tests, and applications in psychology research.
A comprehensive introduction to parametric and non-parametric statistics—their definitions, key differences, and when each approach is appropriate in psychological research.
Comprehensive guide to the major parametric statistical tests—t-tests for two samples, ANOVA for three or more samples, and Pearson's r and Fisher's Z for correlation—including formulas, assumptions, and worked examples.
Understand partial correlation — how to remove the statistical influence of a third variable from a bivariate relationship to reveal the true association between two variables.
Master the computation of Pearson's Product Moment Correlation Coefficient using deviation score and raw score methods, and learn how to test its statistical significance using t-distribution and critical values.
Eight practical applications of the Normal Probability Curve — from determining percentages of cases and percentile ranks to grading systems, overlapping distributions, and test item difficulty analysis.
Master Spearman's Rank Order Correlation (rho) and Kendall's Tau — nonparametric alternatives to Pearson's r for ordinal data, rank data, and situations where Pearson's assumptions are violated.
Comprehensive guide to non-parametric statistics: rationale, assumptions, levels of measurement, comparison with parametric tests, advantages, disadvantages, and when to use which test.
Learn how to test whether a regression slope is statistically significant using the F-test, evaluate prediction accuracy with r², standard error of estimate, and PIP, and understand the key assumptions underlying linear regression.
Master semipartial (part) correlation — controlling a third variable from only one predictor — and Multiple Correlation Coefficient (R), the cornerstone of multiple regression analysis in psychology.
Master the complete step-by-step procedure for setting up levels of significance, defining the critical region, formulating hypotheses, and understanding the producer's and consumer's risk analogy.
Understanding divergence from normality in psychological data — types of skewness and kurtosis, their causes, measurement formulas, and implications for test construction and research in India.
Learn when and how to use Point Biserial Correlation and Phi Coefficient — Pearson-based methods for correlating dichotomous variables with continuous or other dichotomous variables.
Understand the Standard Error of the Mean (SEM), how sample size determines whether to use Z or t distributions, degrees of freedom, and the Standard Error of the Median in psychological hypothesis testing.
Master standardised regression coefficients (beta weights), the multiple regression equation with two or more predictors, ANOVA table for multiple regression, and the hierarchical vs. simultaneous entry methods.
Understand the core logic of hypothesis testing in psychology: the five-step process, null and research hypotheses, comparison distributions, cutoff scores, and Z-scores.
Comprehensive guide to Two-Way ANOVA: main effects, interaction effects, factorial designs, worked examples with 2Ă—2 designs, graphical interpretation, and psychology applications.
Deep understanding of Type I errors (false positives, alpha), Type II errors (false negatives, beta), their relationship, and implications for psychological research and decision-making.