Based on your knowledge of fair decision making and probability concepts, should the Power Pill be produced and distributed? Explain your reasoning. Part 2: What effect (if any) did telling two groups about expected outcomes have on results? Use statistics to provide proof for your reasoning and explain. Part 3: Are there any flaws in the testing process? Should any of the steps or protocol be changed/avoided during round two of testing? Explain your reasoning.

For any product that needs to be released on the market, there should be intensive and rigorous research. Without the research, the safety on the pill will be difficult to determine. A pilot study and survey will be needed to develop the pill. First, the pill needs to be tested on individuals before being released into the market. This can be done by simulating the results using a real pill and the placebo. The effect of the two is compared and the results are analysed. The statistical inferences are done to conclude the comparability in the results.

sophiebophia sophiebophia · Answer 2 · 2022-06-25T05:32:29+02:00

Answer:

Explanation:

Part 1:

Based on my knowledge of fair decision-making and probability concepts, the Power Pill should not be produced and distributed. In trials, 65% ( just the above majority)of people who used the power pill reported seeing results . I believe that there needs to be more testing to make sure the Pill is truly working and worth it.

Part 2:

Telling two groups, which were A and C about expected outcomes had little effect on the outcomes of the results.For example, even though group A was told about expected outcomes they still has a lower success rate than group B (A's 60% vs.B's 70%). However, group C had a higher success rate than their placebo counterpart (C's 50% vs D's 40%) by the same margins as the revivers of the Pill(10%)

Part 3:

There was 1 major flaw that stood out to me during the testing process, which was telling participants about expected outcomes. That could cause an exaggeration of data and maybe even some bias from data collectors.