The analysis of creatinine level given in this case shows a situation where a serious violation of a one-sample t-test assumption occurred causing a need for an alternative method. This emphasizes the importance of testing assumptions. This case also illustrated the importance of visualizing each variable as a necessary first step prior to applying a statistical method. This revealed the left-skewed creatinine distribution. Not only was the one sample t-test inappropriate in this situation, but the median was a better measure of centrality than the mean due to the left-skewed creatinine distribution. While both the one sample t-test and the test of proportion shown here produced the same conclusion, in other circumstances, two different tests may lead to contradictory conclusions.

For this data, it is estimated that 63.7% of patients have creatinine levels indicative of Stage 1 kidney insufficiency. The 95% confidence interval tells us that the true proportion could be as low as 58.7% and as high as 68.4%. This suggests that the hospital should be prepared to handle patients with kidney insufficiency.

Bear in mind that the data was simulated and it is not known the extent to which it represents real patient data on kidney insufficiency. It is always prudent to review results with subject matter experts who can lend valuable insight into the interpretation of the analysis in the problem context. The data set provides additional information on other diagnoses such as diabetes and coronary artery disease. As a next step, relationships between these co-morbidities and kidney function should be examined.