Siksha Sarovar

Siksha Sarovar (sikshasarovar.com) is a free educational web application that helps students in India learn programming and prepare for academic and competitive exams. The platform offers structured coding courses (C, C++, Python, Java, HTML, CSS, PHP, Power BI, AI, Machine Learning, Data Science), complete university curriculum notes for BCA/MCA students with previous year question papers, Class 10 and Class 12 CBSE/HBSE school notes, and dedicated preparation material for SSC, UPSC, Banking, Railway and other government exams. Browsing the site is completely free and requires no account. Users may optionally sign in with Google solely to save their learning progress, quiz scores and personal preferences across devices.

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Siksha Sarovar is a free e-learning platform for coding courses, BCA university notes and competitive exam preparation. Optional Google sign-in saves your learning progress across devices.

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2.1 Unit 2 Overview

Lesson 9 of 32 in the free Data Visualisation and Analytics notes on Siksha Sarovar, written by Rohit Jangra.

Unit 2: Statistical Methods & Hypothesis Testing

This unit covers the fundamental statistical techniques required for data analysis, tailored for BCA and computer science students. You will learn the theoretical foundations of probability distributions, sampling theory, and the rigorous framework of hypothesis testing.

In real-world analytics, statistical methods are used to determine whether patterns found in data are meaningful or just the result of random chance. Mastering these concepts is essential for building reliable predictive models, evaluating machine learning algorithms, and making robust data-driven decisions.

Topics Covered in This Unit

#TopicDescriptionKey Concepts
2.2Probability DistributionsMathematical models for data behaviorNormal, Binomial, Poisson Distributions, PDFs
2.3Sampling TheoryFoundations of population inferenceCentral Limit Theorem, Standard Error, t/F/Chi² Distributions
2.4Hypothesis TestingDecision-making frameworkH₀ vs H₁, Type I/II Errors, Statistical Power
2.5Parametric Tests (Z & T)Testing means and proportionsZ-test, One/Two-sample T-tests, Assumptions
2.6p-Values & SignificanceMeasuring statistical evidencePDF Area, Misconceptions, Alpha Level
2.7Confidence IntervalsEstimating parameters with uncertaintyMargin of Error, Confidence Levels, Precision vs. Accuracy
2.8Non-Parametric: Chi-SquareTesting categorical relationshipsIndependence Test, Goodness of Fit, Contingency Tables
2.9Correlation & RegressionModeling variable relationshipsPearson r, OLS Regression, R² and SSE
2.10ANOVA (Analysis of Variance)Comparing multiple groupsF-Statistic, SSW, SSB, Post-Hoc Analysis
2.11Paradoxes & CaveatsWhen statistics can be misleadingSimpson's Paradox, Base Rate Fallacy, Overfitting