How do I understand core concepts?

# Grasp Null Hypothesis, P-Values, and Errors Start with Khan Academy's 'Significance tests (hypothesis testing)' unit. - Watch videos: 'Simple hypothesis testing', 'Idea behind hypothesis testing', 'P-values and significance tests'. - Complete exercises on writing null/alternative hypotheses and estimating P-values from simulations. - Note: Null (H0: no effect), Alternative (H1: effect exists), Type I/II errors, significance level α=0.05. This builds intuition before math.

How do I learn common test types?

# Explore Z-Test, T-Test, and Chi-Square Read Chapter 11 of 'Learning Statistics with R' (free online). - Sections: Null/alternative, test statistics, p-values. - Focus: Tests for proportions (z), means (t), associations (chi-square). - Watch StatQuest: 'Hypothesis Testing and The Null Hypothesis' (YouTube). Key: Check conditions (normality, independence) before testing.

How do I practice manual calculations?

# Compute Tests by Hand Use Khan Academy exercises and practice problems. - Solve: Z-test for proportion (e.g., coin fair? n=100, heads=62). Formula: z = (phat - p0) / sqrt(p0(1-p0)/n) - T-test for mean: t = (xbar - μ0) / (s/sqrt(n)) - Find p-value using tables or approximations. - Verify with Social Science Statistics calculator. Example from LSR: binom.test(62,100,p=0.5) → p=0.021.

How do I implement with software?

# Run Tests in Python or R Use free Google Colab. - Python (SciPy): from scipy.stats import ttest_1samp; ttest_1samp(data, popmean=0) - R (in Colab or online): t.test(data, mu=0) - Load sample data (e.g., from GitHub notebook: https://github.com/tirthajyoti/Stats-Maths-with-Python/blob/master/Intro_Hypothesis_Testing.ipynb) - Interpret: statistic, p-value, confidence interval. Practice A/B test simulation.

How do I apply to real data and interpret?

# Analyze Datasets and Draw Conclusions - Download practice datasets (e.g., from Khan or StatLibreTexts exercises). - Full process: State H0/H1, check conditions, compute test, interpret p-value/power. - Example: Test if avg height differs (t-test). - Discuss: Effect size (Cohen's d), power analysis. - Review: Avoid p-hacking, report exact p-values. Use calculator for verification: https://www.socscistatistics.com/tests/.

Master Hypothesis Testing: Basics to Application in 5 Steps

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Step-by-Step Guide

Grasp Null Hypothesis, P-Values, and Errors

Start with Khan Academy's 'Significance tests (hypothesis testing)' unit.

Watch videos: 'Simple hypothesis testing', 'Idea behind hypothesis testing', 'P-values and significance tests'.
Complete exercises on writing null/alternative hypotheses and estimating P-values from simulations.
Note: Null (H0: no effect), Alternative (H1: effect exists), Type I/II errors, significance level α=0.05.

This builds intuition before math.

Why this step matters:

-Forms the foundation for all statistical inference decisions
-Enables confident interpretation of test results in experiments

1-2 hours

Khan Academy, Notebook for notes

Definition of Done

•Explain null vs alternative and p-value in own words
•Identify Type I and Type II errors in examples

Common Mistakes to Avoid

❌ Confusing p-value with probability H0 is true

→Remember p-value = P(data | H0 true), not P(H0 | data)

❌ Ignoring significance level α

→Always compare p < α to reject H0

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Suggested Videos

Hypothesis Testing and The Null Hypothesis, Clearly Explained!!!

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Learning Resources

📖 Online Courses

Significance tests (hypothesis testing)

beginner

Khan Academy

Hypothesis Testing

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Great Learning Academy

Hypothesis Testing with Python

intermediate

Paid

Codecademy

📖 Recommended Books

Learning Statistics with R

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by Daniel Navarro

Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions

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by Juan Pablo Sandoval

🌐 Helpful Websites

Khan Academy Significance Tests

tutorial

Interactive videos and exercises

Learning Statistics with R - Hypothesis Testing Chapter

documentation

Detailed explanations with R examples

🛠️ Useful Tools

Social Science Statistics

Calculator

Free calculators for t-tests, chi-square, etc.

SciPy.stats

Programming

Python library for hypothesis tests

Stats Solver Hypothesis Testing

Calculator

Step-by-step calculator

Master Hypothesis Testing: Basics to Application in 5 Steps