Hypothesis testing is a fundamental concept in statistics used to make informed decisions based on data analysis. It involves making an assumption (the hypothesis) about a population parameter and then using sample data to test whether that assumption holds true. The process typically starts with a null hypothesis (H₀), which suggests no effect or no difference, and an alternative hypothesis (H₁), which indicates the presence of an effect or difference. By analyzing data using statistical methods, we determine whether to reject the null hypothesis or fail to reject it, based on a significance level (commonly 0.05).

Hypothesis testing is important because it provides a structured approach to evaluating data and drawing conclusions. It is widely used in scientific research, business decision-making, and quality control to assess the validity of claims or changes. For students, mastering this concept is essential to understanding how data-driven decisions are made in real-world scenarios.

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