Hypothesis testing is a method used to determine if data provides enough evidence to reject an assumption about a population parameter.
Key components include steps in hypothesis testing, significance level (α), type I and type II errors, one-tailed vs. two-tailed tests, and common hypothesis testing methods.
Hypothesis testing involves comparing means of two independent groups, two related samples, expected vs. observed categorical frequencies, and analyzing the relationship between categorical variables.
Understanding hypothesis testing principles, assumptions, and application is crucial for making data-driven decisions in various fields, from web development to machine learning.