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NTA UGC NET Statistics Syllabus 2026, Exam Pattern
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UGC NET Statistics Syllabus 2026, Exam Pattern 2026, Exam Date 2026
Detail Information about UGC NET has published notification 2026 for the recruitment of Statistics. Those Candidates who are Interested to the following Exam and completed all Eligibility Criteria can read the Notification & Apply Online. In this page we provide the Complete Syllabus of this Recruitment with Latest Update Exam Pattern and the Exam Date also.
UGC NET Syllabus 2026 - Highlights
| Particulars | Details |
|
Name of the Exam
|
UGC NET (University Grants Commission National Eligibility Test) |
|
Conducting Authority
|
National Testing Agency (NTA) |
|
Exam Level
|
National Level |
|
Exam Frequency
|
Twice a year |
|
Exam Mode
|
Online |
|
Category
|
Syllabus and Exam Pattern |
|
Language
|
Hindi and English |
|
Purpose of the exam
|
Determine the eligibility of candidates for posts of only Assistant Professor or both Junior Research Fellowship (JRF) and Assistant Professor in Indian universities and colleges |
|
Official Website
|
https://ntanet.nic.in |
UGC NET Syllabus for Statistics 2026
Part I : Teaching/ research aptitude
Unit-I Teaching Aptitude
1. Teaching: Concept, Objectives, Levels of teaching (Memory, Understanding and Reflective), Characteristics and basic requirements.
2. Learner’s characteristics: Characteristics of adolescent and adult learners (Academic, Social, Emotional and Cognitive), Individual differences.
3. Factors affecting teaching related to: Teacher, Learner, Support material, Instructional facilities, Learning environment and Institution.
4. Methods of teaching in Institutions of higher learning: Teacher centred vs. Learner centred methods; Off-line vs. On-line methods (Swayam, Swayamprabha, MOOCs etc.).
5. Teaching Support System: Traditional, Modern and ICT based.
6. Evaluation Systems: Elements and Types of evaluation, Evaluation in Choice Based Credit System in Higher education, Computer based testing, Innovations in evaluation systems.
Unit-II Research Aptitude
1. Research: Meaning, Types, and Characteristics, Positivism and Postpositivistic approach to research.
2. Methods of Research: Experimental, Descriptive, Historical, Qualitative and Quantitative methods.
3. Steps of Research.
4. Thesis and Article writing: Format and styles of referencing.
5. Application of ICT in research.
6. Research ethics.
Unit-III Comprehension
A passage of text be given. Questions be asked from the passage to be answered.
Unit-IV Communication
1. Communication: Meaning, types and characteristics of communication.
2. Effective communication: Verbal and Non-verbal, Inter-Cultural and group communications, Classroom communication.
3. Barriers to effective communication.
4. Mass-Media and Society.
Unit-V Mathematical Reasoning and Aptitude
1. Types of reasoning.
2. Number series, Letter series, Codes and Relationships.
3. Mathematical Aptitude (Fraction, Time & Distance, Ratio, Proportion and Percentage, Profit and Loss, Interest and Discounting, Averages etc.).
Unit-VI Logical Reasoning
1. Understanding the structure of arguments: argument forms, structure of categorical propositions, Mood and Figure, Formal and Informal fallacies, Uses of language, Connotations and denotations of terms, Classical square of opposition.
2. Evaluating and distinguishing deductive and inductive reasoning.
3. Analogies.
4. Venn diagram: Simple and multiple use for establishing validity of arguments.
5. Indian Logic: Means of knowledge.
6. Pramanas: Pratyaksha (Perception), Anumana (Inference), Upamana (Comparison), Shabda (Verbal testimony), Arthapatti (Implication) and Anupalabddhi (Non-apprehension).
7. Structure and kinds of Anumana (inference), Vyapti (invariable relation), Hetvabhasas (fallacies of inference).
Unit-VII Data Interpretation
1. Sources, acquisition and classification of Data.
2. Quantitative and Qualitative Data.
3. Graphical representation (Bar-chart, Histograms, Pie-chart, Table-chart and Line-chart) and mapping of Data.
4. Data Interpretation.
5. Data and Governance.
Unit-VIII Information and Communication Technology (ICT)
1. ICT: General abbreviations and terminology.
2. Basics of Internet, Intranet, E-mail, Audio and Video-conferencing.
3. Digital initiatives in higher education.
4. ICT and Governance.
Unit-IX People, Development and Environment
1. Development and environment: Millennium development and Sustainable development goals.
2. Human and environment interaction: Anthropogenic activities and their impacts on environment.
3. Environmental issues: Local, Regional and Global; Air pollution, Water pollution, Soil pollution, Noise pollution, Waste (solid, liquid, biomedical, hazardous, electronic), Climate change and its Socio-Economic and Political dimensions.
4. Impacts of pollutants on human health.
5. Natural and energy resources: Solar, Wind, Soil, Hydro, Geothermal, Biomass, Nuclear and Forests.
6. Natural hazards and disasters: Mitigation strategies.
7. Environmental Protection Act (1986), National Action Plan on Climate Change, International agreements/efforts -Montreal Protocol, Rio Summit, Convention on Biodiversity, Kyoto Protocol, Paris Agreement, International Solar Alliance.
Unit-X Higher Education System
1. Institutions of higher learning and education in ancient India.
2. Evolution of higher learning and research in Post Independence India.
3. Oriental, Conventional and Non-conventional learning programmes in India.
4. Professional, Technical and Skill Based education.
5. Value education and environmental education.
6. Policies, Governance, and Administration.
Part II : Domain knowledge (Statistics)
Unit I: Probability and Distributions
Basic concepts of probability, conditional probability, Bayes theorem, independent events. Random variables and distribution functions, expectation and moments, moment generating function. Standard discrete and continuous univariate distributions. Jointly distributed random variables, marginal and conditional distributions. Chebyshev inequality. Sampling distributions, transformation of random variables. Characteristic function and its properties. Modes of convergence of random variables, weak and strong laws of large numbers, central limit theorems (i.i.d. case).
Unit II: Real Analysis and Matrix Algebra
Real Analysis: Finite, countable and uncountable sets; sequences of real numbers, convergence of sequences, bounded sequences, monotonic sequences, Cauchy criterion for convergence; Series of real numbers, convergence, tests of convergence, alternating series, absolute and conditional convergence; Power series and radius of convergence; Functions of a real variable: Limit, continuity, monotone functions, uniform continuity, differentiability, Rolle’s theorem, mean value theorems, Taylor’s theorem, L Hospital’s rule, Riemann integration and its properties, improper integrals.
Functions of two real variables: Limit, continuity, partial derivatives, total derivative, maxima and minima, saddle point, method of Lagrange multipliers, double and triple integrals and their applications.
Matrix Algebra: Vector spaces, subspaces, span, linear independence, basis and dimension, row space and column space of a matrix, rank and nullity, row reduced echelon form, trace and determinant, inverse of a matrix, systems of linear equations; Gram-Schmidt orthogonalization; Characteristic roots and characteristic vectors, characteristic polynomial, Cayley-Hamilton theorem, symmetric matrices, skew-symmetric matrices, orthogonal matrices and their characteristic roots, positive definite and positive semi-definite matrices and their properties, quadratic forms.
Unit III: Sampling Methods and Design of Experiments
Sampling Methods: Simple random sampling, stratified random sampling, systematic sampling. Ratio and regression methods of estimation, cluster sampling for equal and unequal clusters, double sampling, sampling with varying probabilities with and without replacement. Nonnegative variance estimation, ordered and unordered estimators.
Design of Experiments: Analysis of variance in one-way and two-way classification (with and without interaction) in fixed effects model, principles of design of experiments, completely randomized design, randomized block design, Latin square design, missing plot techniques. Factorial experiments-22, 23, confounding in factorial experiments, Incomplete block designs and its intra-block and inter-block analysis, connectedness and orthogonality of block designs, balanced incomplete block design (BIBD), inter-block analysis and recovery of intra-block information of BIBD.
Unit IV: Estimation Theory
Point Estimation: Unbiasedness, consistency, method of moments and maximum likelihood estimators, efficiency, uniformly minimum variance unbiased estimators, Rao-Cramer lower bound, sufficiency, factorization theorem, minimal sufficiency, ancillary statistic, completeness, Rao-Blackwell theorem, Lehmann-Scheffe theorem, Basu’s theorem.
Interval estimation: method of pivoting, confidence intervals for parameters in one sample and two sample normal populations. confidence intervals based on large samples.
Nonparametric Inference: Distributions of order statistics, empirical distribution function and its properties. Rank correlation coefficients of Spearman and Kendall.
Unit V: Testing of Hypotheses
Basic concepts, construction of tests: Neyman-Pearson lemma, families with monotone likelihood ratio. Uniformly most powerful, uniformly most powerful unbiased and uniformly most powerful invariant tests, likelihood ratio tests: applications to one sample and two sample problems. Wald’s sequential probability ratio test, operating characteristic and average sample number.
Chi-square tests (goodness of fit, independence of attributes, homogeneity in contingency tables), sign test, Wilcoxon signed rank test, Mann-Whitney U-test, linear rank tests for location and scale problems, Kruskal-Wallis test.
Unit VI: Linear Estimation, Regression Analysis and Econometrics
Simple and multiple linear regression model, Gauss-Markov model, least squares and maximum likelihood estimation, testing of hypothesis related to regression parameters, Analysis of variance for linear model, R2, adjusted R2, tests of linear hypothesis, generalized and weighted least squares estimation, indicator/dummy variables, multicollinearity, heteroscedasticity, autocorrelation, Durbin-Watson test, logistic regression models.
Restricted regression estimation under exact, stochastic and mixed restrictions. Model with stochastic regressors and errors in variable model, instrumental variable estimator, simultaneous equations model, identification problem, two-stage least squares estimation, k-class estimator.
Unit VII: Time Series
Time series data, descriptive measures, autocovariance, autocorrelation functions (ACVF, ACF), and partial autocorrelation function (PACF), correlogram. Strong and weak stationarity, ergodicity. General linear process and Wold decomposition. Moving Average (MA), Autoregressive (AR) and mixed ARMA processes, stationarity and invertibility conditions. Yule–Walker equations. Identification, estimation and order selection of AR, MA and ARMA models, forecasting with stationary and invertible processes.
Non-stationary time series: random walk, ARIMA (p, d, q) models and parameter estimation. Frequency domain analysis: Spectral representation of time series, spectral density of AR, MA and ARMA processes, periodogram analysis and estimation of spectral density.
Unit VIII: Multivariate Analysis
Multivariate normal distribution and its properties, estimation of mean vector and covariance matrix in multivariate normal distribution, distribution of sample mean vector, Wishart distribution and its properties, distribution of simple, partial and multiple correlation coefficients and related tests, inference for parameters. Test of hypothesis related to mean vector and generalized statistic, discriminant analysis, principal component analysis, canonical correlation analysis.
Unit IX: Stochastic Processes
Markov chains with finite and countable state space, classification of states, Chapman Kolmogorov equations, limiting behaviour of n-step transition probabilities, stationary distribution, Gambler’s ruin problem, simple random walk. Poisson process, inter-arrival and waiting time distributions, Birth and death processes, M/M/1 queues.
Unit X: Indian Statistical System and Research Methodology
Indian Statistical System: Ministry of Statistics and Programme Implementation and its different wings, National Statistical Commission, National Statistics Office, census and large sample surveys. Contributions of P C Mahalanobis, P V Sukhatme, R C Bose, S N Roy, C R Rao, and other prominent Indian Statisticians.
Research Methodology: ‘R’ software: R as a calculator, functions and matrix operations, built in functions, missing data and logical operators. Conditional executions and loops; data management with sequences, repeats, sorting, ordering and strings; lists, factors, display and formatting. Data frames, data input and output, graphics and plots. Basics of programming, scripts and functions. Latex and other word processing software.
UGC NET Statistics Exam Pattern 2026
Duration : 03 hours (180 minutes)
Exam Medium : HIndi & English
Exam Medium : HIndi & English
| Part | Subject | No.of Question | Marks |
| Part I |
Teaching/ research aptitude of the candidate.
It will primarily be designed to test reasoning ability, reading comprehension, divergent thinking and general awareness
|
50 | 100 |
| Part II | Domain knowledge (Statistics) | 100 | 200 |
| Total | 150 | 300 |
UGC NET Exam Date 2026 : 22nd to 30th June 2026
Starting Date of Application Form : 29th April 2026
Last Date of Application Form : 20th May 2026
FAQs
NTA UGC NET Statistics 2026 Exam Syllabus Frequently Asked Questions (FAQ's)
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In this article Page, we have provided the required syllabus of the UGC NET Statistics exam.
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