How to design hypothesis testing in quantitative research?

Hypothesis testing is a statistical method used in quantitative research to objectively evaluate conjectures about population parameters based on sample data. It systematically assesses the evidence for or against a specific prediction. Designing robust hypothesis testing necessitates defining clear, testable null (H₀) and alternative (H₁) hypotheses upfront. Researchers must select an appropriate statistical test (e.g., t-test, chi-square, ANOVA) aligned with the data type, distribution assumptions, and research question. Determining the significance level (α, typically 0.05), choosing a one-tailed or two-tailed test, and calculating sufficient sample size for adequate power are essential prerequisites. Assumptions underlying the chosen test must be validated to ensure result reliability. The key implementation steps begin with precisely formulating H₀ and H₁. Next, collect data meeting the test's assumptions and compute the relevant test statistic. Determine the critical region or p-value based on α. If the test statistic falls in the critical region or the p-value is less than α, reject H₀ in favor of H₁; otherwise, fail to reject H₀. Results are interpreted cautiously within context, considering effect size and confidence intervals for practical significance.