Use attached Word Doc and enter responses in RED. This is a 3 part assignment. This is for a class discussion thread so the “Discussion Responses 1 & 2” need to be conversational. Note the word count requirement for each part. Main Point Discussion Explain how increasing alpha increases statistical power. (Add Main Point Discussion here – 200 words or more – Use examples, or charts, etc. to build on the Discussion Point) Discussion Response 1 Breanna Webster – Question 4 What does statistically significant mean to statisticians? When reading through a peer-reviewed journal article regarding a study or experiment that has been conducted, you will most likely come across a statement that the obtained data is statistically significant or insignificant. What does this mean to statisticians? It does not mean that the data is either important or unimportant. Instead, when data is statistically significant it simply means that the findings would be unlikely to occur if the null hypothesis were true (Nolan & Heinzen, 2014). In other words, it shows that there is a difference or relationship between the variables studied. Statistical significance can be affected by sample size. For example, let’s say that we give 5,000 people a test that measures openness to experience. We want to know if there is a difference between the scores of females and those of males. It was discovered that the average score (or mean) for females is 50 and the average score for males is 48. By running a z-test and confidence interval test, we find that the difference is significant. The course textbook provides a formula that can be used to complete this process (Nolan & Heinzen, 2014). However, if the same test were given to 100 people the difference between gender groups would not have been significant. A larger sample size allows for small differences to be recognized as significant. (Add Discussion Response 1 here – 100 words or more – Add some feedback to build on the discussion) Discussion Response 2 Emma Hernandez – Question 11 How does statistical power relate to Type II errors? Statistical power is “the probability that we will reject the null hypothesis when we should reject the null hypothesis” (Nolan & Heinzen, 2014, p. 206). Type II error is incorrectly retaining a false null hypothesis (a “false negative”). Statistical power is inversely related to Type II errors because it is the probability that we won’t make a Type II error. In mathematical terms, this is expressed as: power = 1 – β. If statistical power is high, the probability of making a Type II error, or concluding there is no effect when, in fact, there is one, goes down. (Add Discussion Response 2 here – 100 words or more – Add some feedback to build on the discussion)