5 Important Non-Parametric Tests for Correlated Data and Uncorrelated Data

Introduction Non-parametric tests provide robust alternatives to parametric methods when data violate assumptions such as normality or homogeneity of variances. These tests are invaluable when dealing with ordinal data, small Read More …

Parametric and Non-Parametric Statistics: 6 Important Differences Between Them

Introduction Statistics is a powerful tool for analyzing, interpreting, and making inferences about data. Two fundamental branches of statistical analysis are parametric and non-parametric statistics. Understanding the differences, assumptions, and Read More …

Chi-Square and 6 Important Assumptions of Chi Square

Introduction Chi-square (χ²) tests are among the most widely used non-parametric statistical methods for analyzing categorical data. They provide a versatile set of tools for testing hypotheses about frequencies in Read More …

ANOVA and 3 Important Assumptions of It

Introduction Analysis of Variance (ANOVA) and its variants are foundational techniques in inferential statistics used to compare means across groups and evaluate complex relationships between variables. Read More- Factor Analysis Read More …

3 Important Significance of Differences- Means, Variances, and Correlation Coefficients

Introduction Understanding the significance of differences in statistical measures such as means, variances, and correlation coefficients is essential in psychology and other behavioral sciences. These differences help researchers determine whether Read More …

Standard Error of the Mean (SEM) Statistics and 3 Important Factors Affecting It

Introduction to Statistical Inference Statistical inference allows scientists to make generalizations about populations using data drawn from samples. It is based on probability theory, specifically the behavior of sampling distributions. Read More …

Linear Regression and 4 Important Assumptions of It

Introduction Simple linear regression is a foundational statistical method used to model the relationship between two continuous variables: one independent (predictor) and one dependent (outcome). It helps predict the outcome Read More …

Partial and Multiple Correlation: 2 Important Types of Correlations

Introduction In behavioral sciences, understanding relationships between variables is critical. While simple correlation helps establish a linear relationship between two variables, real-world phenomena often involve more complex interdependencies. For example, Read More …

Point-Biserial, Phi Coefficient, Biserial, and Tetrachoric Correlation: 4 Important Types of Correlation

Introduction In psychological and educational research, not all variables are continuous. Many important variables — such as gender, pass/fail outcomes, yes/no responses, or item correctness — are dichotomous (i.e., having Read More …

Correlation: Concept, Meaning, and 3 Important Ideas in Its Nature

Introduction Correlation is a foundational statistical concept used to examine the degree and direction of association between variables. It allows psychologists, educators, and social scientists to answer critical questions about Read More …

Counsellors (Contract Basis) at Navodaya Vidyalaya Samiti (NVS)

Navodaya Vidyalaya Samiti (NVS) – Regional Office Pune Jawahar Navodaya Vidyalayas (JNVs) – fully residential, co-educational schools with classes VI to XII – invite online applications from eligible candidates for Read More …

Neuropsychological Rehabilitation and 4 Important Goals of It

Introduction Neuropsychological rehabilitation stands at the intersection of neuroscience, psychology, and functional therapy. It is a systematic and patient-centered process intended to help individuals regain as much of their cognitive, Read More …