What is hypothesis testing and how is it applied in research?

Hypothesis testing is a formal statistical procedure used to evaluate claims about population parameters using sample data, determining whether evidence supports a specific research hypothesis. It provides a structured framework for making inferences and drawing conclusions based on empirical evidence. The process involves formulating a null hypothesis (H₀), representing no effect or status quo, and an alternative hypothesis (H₁), representing the researcher's claim. A significance level (α, typically 0.05) is set to define the probability threshold for rejecting H₀. The test statistic calculated from the sample data is compared to a critical value or used to compute a p-value; the p-value represents the probability of observing the sample data, or more extreme, if H₀ is true. If the p-value is less than α, H₀ is rejected in favor of H₁, indicating statistically significant evidence for the research hypothesis. Key assumptions, such as data distribution and independence, must be validated. Hypothesis testing is fundamental across scientific research for evaluating theories and interventions. Applications include assessing drug efficacy in medicine, examining policy impacts in social sciences, verifying engineering tolerances, and validating economic models. It quantifies evidence strength, supporting objective decision-making and advancing knowledge by systematically challenging proposed relationships or effects based on collected data.