Interpreting Statistical Results for High-Quality Research Reporting

@misc{rogesh2026interpreting,
  author       = {{ROGESH}},
  title        = {{Interpreting Statistical Results for High-Quality Research Reporting}},
  year         = {2026},
  month        = oct,
  date         = {2026-10-29},
  publisher    = {Life of Research},
  url          = {https://lifeofresearch.com/articles/154},
  abstract     = {Statistical analysis plays an important role in improving the accuracy, reliability, and quality of research findings. However, researchers often face difficulties in interpreting p-values, selecting appropriate statistical tests, and reporting results correctly. This paper presents a structured framework for interpreting and reporting statistical results in quantitative research. It explains the importance of hypothesis testing, effect sizes, confidence intervals, and statistical assumptions. Different statistical methods, including t-tests, ANOVA, Mann–Whitney U tests, multiple linear regression, and chi-square tests, are discussed with suitable reporting practices. The paper emphasizes transparent data interpretation, accurate presentation of results, and the use of established reporting guidelines to improve research quality and reproducibility. The proposed framework helps researchers, reviewers, and journal editors communicate statistical findings clearly and effectively.},
  keywords     = {Statistical Reporting, Data Interpretation, Hypothesis Testing, P-values, Effect Size, Confidence Intervals, Quantitative Research, ANOVA},
  language     = {en},
  copyright    = {Creative Commons Attribution 4.0}
}