Final answer:
To clarify the pilot study's findings on Alzheimer's meds, consider the sample size, the power of the study, and type II error. Use a TI-83 series calculator to perform t-tests or ANOVA with your data for statistical significance. Example 10.11 shows a similar case with non-significant results.
Step-by-step explanation:
To clarify a small pilot study that measures changes in MMSE scores to compare the efficacy of two medications for the treatment of Alzheimer's, you need to consider the sample size, the power of the study, and the possibility of type II error. When the data suggests a difference between the medications, but it is not statistically significant, it could mean that the study did not have enough participants to detect a real effect, or that the effect size is too small. It is also possible that there is indeed no difference, but this can only be confirmed with a larger, more definitive study.
Using a TI-83, 83+, 84, 84+ Calculator, you can perform the statistical tests required to investigate the efficacy of the medications. For instance, you might conduct a t-test or an analysis of variance (ANOVA) to determine if the differences observed in the MMSE scores are statistically significant at a specified significance level, such as 5% (0.05). The output, including the p-value, will inform whether the null hypothesis can be rejected or not.
Example 10.11 from the data provided is relevant because it shows a scenario where pain-reducing medication did not provide a statistically significant reduction in pain at a 5 percent significance level, which is similar to the scenario with the Alzheimer's medications. A lower p-value (less than the alpha level) would reject the null hypothesis and suggest there is a statistically significant difference.