Examinations
Each exam’s syllabus, line by line, pointed at the page that teaches it. All examinations
UGC NET Statistics
Subject code 107, all ten units written out in full, every line of the official syllabus mapped onto them — and the part most people come for: a model MCQ bank and a solved paper, each answer explained rather than just marked.
10 units · 500 model MCQs · a solved paper of 150Browse → NET examCSIR NET Mathematical Sciences
A statistics candidate sits Units 1 and 4. Both are mapped line by line onto the courses here; Units 2 and 3 are named as the mathematics candidate’s and left out.
Syllabus map · gaps namedOpen map → NET examASRB NET Agricultural Statistics
Eight units, from measure-theoretic probability to optimisation and soft computing, every line pointed at the course that teaches it — and Statistical Genetics marked red, because this site does not teach it.
Syllabus map · gaps namedOpen map → Other examIndian Statistical Service
The widest statistics syllabus that is also fixed, so the site maps itself against it once: all four statistics papers, every line pointed at the page that teaches it.
Four papers mapped · gaps namedOpen map → Other examAPPSC Assistant Director & Assistant Statistical Officer
Both posts share one syllabus word for word, so one map serves both and says which paper each line sits in.
One map, two posts · solved 2025 & 2022 papersOpen map →Topics A–Z
Search 2,157 topics and every page, or pick a letter. The answer appears here.
Press / to search from anywhere on this page.
Pick a letter to list its topics here, each with the pages that teach it.
Browse all topics A–ZStudy material
Every course stands on its own, with a contents page listing every unit.
Statistics Courses
One catalogue of courses in learning order, from descriptive statistics to multivariate analysis. Study any course, in any order, for the exam you are preparing for.
Foundations · Probability and distributions · Statistical inference · Sampling and design · Models and multivariate · Applied statistics · Statistical computing · Allied: economics and financial accounting
36 courses · 153 unit pagesBrowse →Data Science Courses
Nineteen courses grouped by topic, with every prescribed lab experiment as source you can compile and run.
C · Python · Statistics · DBMS · R · Web technologies · Data mining · MongoDB · Business intelligence · Machine learning · AI · Big data · Cloud · Deep learning · Time series · NLP · MLOps
19 courses · 5 topic groupsBrowse → Practical guideWhich Statistical Test to Use
The question people actually arrive with. What you are asking, what your data looks like, and what to use instead when the assumptions fail.
Pearson’s r · Spearman’s rank · Regression · One-sample and paired t-tests · Independent t-test · One-way and two-way ANOVA · z-test · F-test · Chi-square goodness of fit and independence · Fisher’s exact · Mann–Whitney · Wilcoxon · Kruskal–Wallis · Sign test · Run test
13 tests · assumptions · alternativesOpen →Start here
The pages people usually want first.
How this material is written
Every step is shown
No "clearly" or "it follows that" standing in for a step. Proofs name the tool each line uses; problems show the setup, the substitution and the arithmetic.
Examples, not just results
Worked examples throughout, and at least two per concept in the exam material — the answer always ends with what it actually means.
The code runs
Lab programs are real source in C, Python, SQL and R, checked by compiling and running them rather than by reading them.
Gaps are stated
Where a syllabus omits something the exam still asks, it is covered and flagged. Where something was not verified, that is said plainly instead of hidden.