Final answer:
Using a 95% confidence interval to analyze the association between the number of comorbid chronic diseases and MMSE score helps to determine if the observed association is statistically significant or due to random sampling variability.
Step-by-step explanation:
When analyzing the association between the number of comorbid chronic diseases and MMSE (Mini-Mental State Examination) score, using a 95% confidence interval is helpful to determine if the observed association is due to random sampling variability or if it is statistically significant. If the 95% confidence interval around the estimated association does not include the value suggesting no association (often zero for difference measures or one for ratio measures), then it's less likely that the observed association is due to chance. Instead, it would suggest that there's a statistically significant association between the variables being studied.
Random sampling variability refers to the variation that is inherent when taking a random sample from a population. The key is to determine whether the observed pattern holds true for the larger population from which the sample was drawn. If the 95% confidence interval is narrow and does not cross the null value, we can be more confident that the observed association is likely to reflect a real relationship in the population, rather than just random chance.
For example, if we took 100 different samples and computed a 95% confidence interval for each, we would expect about 95 of these intervals to contain the true population mean of the MMSE scores.