For the following studies, think about everything that was done wrong in the ana

Question

For the following studies, think about everything that was done wrong in the analysis. Then describe how you would do the analysis if you were in charge.

2. Students at University of Winterhold study either Destruction Magic, Illusion Magic, or Healing Magic. The President of the University has noticed that 72% of Destruction Magic students and 78% of Illusion Magic students are graduating, while only 62% of Healing Magic students graduate. He does an ANOVA test to see if these means are equal. The test is significant, so he fires all his Healing Magic teachers.

3. Cerebrus is a powerful company that manufactures weapons. The CEO decides to do a poll to see if customers are likely to buy his new product, the Shepard 2.0. He mails a survey to every registered address on file. When he gets enough results, he does a t-test to see if more than 50% of customers will buy the product.

Explanation / Answer

Sample study 2

The impact of following were not considered.

1. The total number (n) of students enrolled in each stream of study in consideration.

2. The sample points of three streams need not be independent. for eg. there might be students who passed any two or even all three streams of study. But due to ANOVA analysis which considers samples as random samples, this effect is not considered.We need to ensure that the samples are normalized. So we shouls convert these samples to normal .

3. We are considering the distribution to be normal ,which may not be the case. the samples may be heavily biased. We need to counter for the sample biasness by adding the sample bias to each of three sample.

4. The ANOVA test only tell us whether at least one stream of study in our analysis is different from at least one other. It does not tell as to how many groups, or which groups, differ statistically. In order to determine this, follow-up comparisons must be performed. This leads to the chance of making a Type I error, which is assuming an effect where there isn't one.

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