Interpreting results involves more than just understanding statistical significance. Researchers must consider the effect size, confidence intervals, and the practical significance of the findings. It's essential to distinguish between correlation and causation and to be aware of potential confounding factors. Sensitivity analyses and robustness checks are also vital to ensure the reliability of the results.
