{"id":656,"date":"2023-07-13T12:19:08","date_gmt":"2023-07-13T12:19:08","guid":{"rendered":"https:\/\/www.editage.com\/blog\/?p=656"},"modified":"2023-07-19T07:25:59","modified_gmt":"2023-07-19T07:25:59","slug":"statistical-practices-to-generate-robust-research-data","status":"publish","type":"post","link":"https:\/\/www.editage.com\/blog\/statistical-practices-to-generate-robust-research-data\/","title":{"rendered":"5 Statistical Practices You Need to Generate Robust Research Data\u00a0"},"content":{"rendered":"\n

When it comes to both designing and reporting rigorous research, statistical practices play a crucial role in ensuring the validity and reliability of your findings. Faulty statistical analysis<\/a> can torpedo your entire research project. So, let\u2019s dive into five powerful statistical practices that will raise the quality of your conclusions and lend it the scientific validity it deserves. <\/p>\n\n\n\n

  1. Calculate Statistical Power A Priori: The Power of Being Prepared<\/strong> <\/li><\/ol>\n\n\n\n

    Imagine this: you design a study, collect data, and analyze it\u2014only to find that your sample size was too small to detect meaningful effects. Frustrating, right? That\u2019s where statistical power comes to the rescue. By calculating statistical power<\/a> before starting your study, you can determine the minimum sample size needed to detect the effects you\u2019re interested in. It\u2019s like equipping your research with a magnifying glass to spot even the tiniest yet significant findings. So, plan ahead and harness statistical power! <\/p>\n\n\n\n

    1. Deal with Missing Data Appropriately: Plug the Bathtub Before Filling It!<\/strong> <\/li><\/ol>\n\n\n\n

      Missing data can be a pesky issue, but it doesn\u2019t have to compromise your research. It\u2019s important to handle missing data<\/a> appropriately to avoid bias and maintain the integrity of your findings. Explore different imputation methods or consider statistical techniques designed specifically for missing data analysis. By carefully addressing missing data, you\u2019ll ensure that your conclusions are based on an accurate representation of your study population. <\/p>\n\n\n\n

      1. Run a Test Only After Verifying Assumptions: Assumptions Matter<\/strong> <\/li><\/ol>\n\n\n\n

        Choosing the right statistical test is crucial, but it\u2019s equally important to verify that all the assumptions<\/a> associated with that test are met. Assumptions often hide in plain sight, and ignoring them can lead to erroneous conclusions. Take a moment to check whether your data satisfy assumptions such as normality, homogeneity of variance, and independence. If they don\u2019t, fear not! There are alternative tests or transformations that can help you accurately analyze your data. Be diligent in ensuring your assumptions are met to build a solid statistical foundation for your research. <\/p>\n\n\n\n

        1. Calculate Effect Sizes and Confidence Intervals: Go Beyond P Values!<\/strong> <\/li><\/ol>\n\n\n\n

          Ah, P values<\/a>\u2014they\u2019ve been researchers\u2019 favorite data for a long time. However, relying solely on P values can be misleading. Effect sizes and confidence intervals provide valuable additional information about the magnitude and precision of your findings. Effect sizes tell you the practical significance of your results, while confidence intervals<\/a> give you a range of plausible values for the population parameter. So, don\u2019t forget to report effect sizes and confidence intervals alongside those P values to present a more complete picture of your research outcomes. <\/p>\n\n\n\n

          1. Embrace Open Data Best Practices: Sharing Is Caring (for Science)!<\/strong> <\/li><\/ol>\n\n\n\n

            In this era of collaborative science, embracing open data<\/a> practices can revolutionize the way we advance biomedical research. By making your data openly available, you enable others to replicate, verify, and build upon your work. Plus, data sharing<\/a> fosters transparency and trust, and accelerates scientific progress. So, consider sharing your de-identified data, code, and methodologies with the scientific community. You\u2019ll contribute to collective knowledge and could leave a lasting impact on your field. <\/p>\n\n\n\n

            Conclusion<\/strong> <\/p>\n\n\n\n

            Calculating statistical power, handling missing data appropriately, verifying assumptions, reporting effect sizes and confidence intervals, and embracing open data practices are all vital steps toward ensuring the integrity and impact of your work. By incorporating these five statistical practices into your biomedical research, you\u2019ll strengthen the rigor and credibility of your findings, and boost your career as a scientist. <\/p>\n\n\n\n

            Let\u2019s embark on this statistical journey together and make biomedical research shine brighter than ever before!<\/em> Consult an expert biostatistician under Editage\u2019s <\/em>Statistical Analysis & Review Services<\/em><\/a>. <\/p>\n","protected":false},"excerpt":{"rendered":"When it comes to both designing and reporting rigorous research, statistical practices play a crucial role in ensuring the validity and reliability of your findings. Faulty statistical analysis can torpedo your entire research project. 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