Interpretation in research studies refers to the process of making sense of collected data by assigning meaning to statistical results in the context of research objectives, hypotheses, and theoretical frameworks. It goes beyond mere presentation of numbers and involves explaining relationships, patterns, and implications derived from data analysis. In Research Methodology and Statistical Analysis, interpretation is a crucial step because it transforms raw statistical outputs into meaningful insights that contribute to knowledge. The researcher must carefully analyze statistical measures such as mean, median, standard deviation, correlation coefficients, regression outputs, and significance levels, and relate them to the research problem. Proper interpretation requires an understanding of both statistical techniques and subject matter knowledge, ensuring that conclusions are logically derived and consistent with the data. Interpretation must consider the limitations of the study, including sample size, sampling technique, measurement errors, and potential biases. For instance, a statistically significant result does not necessarily imply practical significance, and researchers must distinguish between the two. Misinterpretation can arise when statistical significance is overemphasized without considering effect size or real-world implications. Additionally, contextual factors such as time, location, and population characteristics must be taken into account to avoid misleading conclusions. Interpretation also involves comparing findings with existing literature, thereby validating or challenging previous research and contributing to theoretical advancement. Generalization refers to the extent to which the findings of a research study can be applied to a broader population beyond the sample studied. In statistical terms, generalization is closely related to external validity. It depends largely on the sampling design and the representativeness of the sample. Probability sampling techniques, such as simple random sampling, stratified sampling, and cluster sampling, enhance the ability