Final answer:
An odds ratio confidence interval not containing 1 indicates an association; an odds ratio above 1 implies increased risk. A 95% confidence interval allocates 2.5% probability to each tail. A p-value higher than the significance level means not rejecting the null hypothesis.
Step-by-step explanation:
If a 95% confidence interval for an odds ratio does not contain 1, this indicates that there is evidence of an association between the exposure and the outcome. Specifically, if the odds ratio is greater than 1 and the confidence interval does not include 1, there is evidence of increased risk with exposure. Conversely, if the odds ratio is less than 1 and the confidence interval does not include 1, there is evidence of decreased risk with exposure. In this scenario, the odds ratio is 3.25, suggesting that the exposed group is 3.25 times more likely to experience the health event compared to the non-exposed group.
Regarding the construction of a 95 percent confidence interval, 5 percent of the total probability is excluded, split evenly at 2.5 percent in each tail of the distribution. If we created 100 confidence intervals, we would expect 95 of them to contain the true population mean. When observing real-world data, the larger the number of trials, the closer the observed outcomes should match the expected probability.
For the probability calculation, the p-value of 0.2150 implies that if the significance level is set at 5 percent, we would not reject the null hypothesis as the p-value is greater than 0.05. We would conclude that there is no statistically significant evidence against the null hypothesis.