Learn how to conduct a one-sample T-test for a population mean using hypothesis testing, T-scores, and critical values. Understand when and why to use the T-distribution.

Learn how to perform a one-tailed or two-tailed Z-test for a population proportion. This guide walks through hypothesis setup, Z-score calculation, and P-value interpretation with a real-world case.

Learn how to formulate null and alternative hypotheses, understand their role in statistical significance testing, and apply them through real-world examples like click-through rates and server response times.

Learn to calculate confidence intervals using two fresh examples — one for estimating an average (mean) and one for estimating a percentage (proportion) in real-world data science scenarios.

Learn how to calculate the ideal sample size needed for your survey or experiment using step-by-step examples and formulas. Perfect for means or proportions.

Understand 90%, 95%, and 99% confidence intervals with real-world examples, Python code, and ML use cases. Learn how confidence levels affect interval width and precision.

Learn how to build a confidence interval for a population proportion step by step. Ideal for binary data, surveys, and evaluating classification accuracy in machine learning. Includes Python examples.

Learn how to construct a confidence interval for a population mean when the standard deviation is unknown using the t-distribution.

Learn how to calculate a confidence interval when the population standard deviation is known using a real-world example on teen screen time.

Learn the basics of Inferential Statistics. Discover how to estimate population parameters from samples using Point Estimation and Confidence Intervals to make data-driven decisions.