Statistical Significance vs. Clinical Importance. #biostatistics #shorts

Main Video: https://youtu.be/LCneQMG2uEo So statistical significance alone does not indicate the magnitude of clinical importance of the observed difference. It is with every statistical tool that you use that you cannot commit anything without effect size. Complexity increases with advanced design, so interpretation becomes more challenging with factorial repetitive measures and mixed Ennova models. Now common errors and pitfalls. So using multiple T-tests instead of INNOVA, a lot of tags, we use multiple T-tests. Inula, he is ignoring the INNOVA assumptions again. Like I said, you can't commit to the result itself, but it is up to the researcher and the statistician in degrading a significant without the most significant theme. Postdoc Nelagaa is focusing only on the p-value. Again, this was something that was covered in the All About p-value seminar. Also, the p-value alone cannot suffice for your whole study. You have to say something more than that. Also using an inappropriate type of equipment. This is something that is common for our statistics; you have to use the right type of it, ignoring violations of homogeneity. O variance failure to address the outlier at equating non-significance with no difference. #StatisticalSignificance #ClinicalImportance #Biostatistics #dataanalysis #researchmethods #evidencebasedpractice #healthstatistics #clinicalresearch #quantitativeresearch #statisticalanalysis