Because we are constantly inferring results from a sample to a population, inferential statistics inherently have
error. This error is related to the hypothesis testing that is the foundation of inferential statistics. There are two
basic types of error called Type I and Type II. It is necessary to understand the difference between the two types
of error since one is more preferable than the other.
Write a one-page response addressing the following:
Identify the types of error in the two vignettes provided. Preview the document Discuss why you selected the type of errors that you did.
Dr. D is a researcher interested in the connections between video game playing and grades in school. He forms two groups the Problem Gamers and the Non-Problem Gamers. Dr. D’s null hypothesis is that there is no difference in the GPA of the two groups. After gathering loads of data, Dr. D is ready to compute his statistical analyses. After crunching the numbers he confidently announces that Problem Gamers do much poorer in school and therefor he rejects his null hypothesis. However, he was unaware that a computer virus caused a significant error in his computations and in fact there is no difference between the GPA of the Problem Gamers and the Non-Problem Gamers.
Dr. D is at it again. This time he is curious about the impact of church attendance on obesity. He forms two groups the Church Attenders and the Non-Church Attenders. Dr. D’s null hypothesis is that obesity rates are the same for both groups. After weighing hundreds of people Dr. D is ready to crunch the numbers. His results indicate that there is no significant difference between the two groups. Dr. D proclaims that church does not in fact cause obesity. However, due to accidentally sampling the “cross fit” group at a local church, his church obesity data was not an accurate representation of average church attenders. In fact, there was a difference in obesity rates between Church Attenders and the Non-Church Attenders.