t-est Analysis

For a course, I need to understand superficially an applied statistical approach in a research paper. The data presented was, twenty-two percent (n = 44) of diabetic patients had metabolically confirmed B 12 deficiency. Patients on metformin had lower serum B 12 levels (425.99 pg/mL vs 527.49 pg/mL; P = .012) and were at increased risk for B 12 deficiency (P = .04), as defined by a serum B 12 level <350 pg/mL. The t test tells you how significant the differences between groups are. It tells you if those differences (measured in means/averages) could have happened by chance. The p-value is the probability of obtaining results as extreme as the observed results of a statistical hypothesis test, assuming that the null hypothesis is correct. A smaller p-value which is presented in the data for this study means that there is stronger evidence in favor of the alternative hypothesis. The null hypothesis states that there is no relationship between the two variables being studied (one variable does not affect the other). Therefore, for this study since evidence is in favor of the alternative hypothesis there is a relationship between diabetes and vitamin B12 deficiency. I was wondering if this analysis seems correct?



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Less is more. Stay pure. Stay poor.
Seems about correct. Side note, it has a "statistically significant" difference, though how does the difference feel as being clinically important? Was there a big difference between groups?
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