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A researcher determines the appropriate type of statistical analysis by first considering the purpose of the study and the research questions being asked. According to Farmer and Farmer (2020), the type of data collected plays a major role in this decision. Researchers must identify whether their variables are categorical or numerical and whether the data is measured at a nominal, ordinal, interval, or ratio level. The study design also matters, including whether the goal is to describe data, compare groups, or examine relationships between variables. Sample size and distribution are additional factors, as some statistical tests require larger samples or assume normal distribution to produce accurate results. Farmer and Farmer (2020) also emphasize the importance of aligning statistical analysis with the hypotheses being tested, since different tests are designed to answer different types of questions. Using an inappropriate statistical method can lead to misleading conclusions, even if the data is collected carefully. By thoughtfully examining the research question, variable types, and study design, researchers can select analyses that provide meaningful and trustworthy results. This careful process strengthens the validity of findings and ensures that research conclusions can responsibly inform practice and policy.