I am dedicated to finding comfort in inferential statistics practice and application.
My entire week has been a major consideration toward over thought on my own part. This week’s assignment has been up on my computer for over four days. I took hours each day to run the SPSS programs and compare the work with a calculator. It helps me if I understand the process. I know that in the end, I got the first problem to work. I was so excited. After that, I decided to show both examples in my final paper. I am still not sure why except to try to show my desire to understand. I will be devoting the remainder of my week to “feeling” knowledgeable in inferential statistics next week. I understood the errors better than I understand how to express the findings of the study. This week I am beaten.
Tokunaga (2016) reminds us that we are expanding our understanding of the research process and, in particular, the process of hypothesis testing. This becomes evident when I read the excellent application of statistics into the justice system. I really appreciate our peer discussions because it helps me understand how to communicate a test's outcome clearly and concisely.
This week has been the greatest challenge to date in this course. I am ready for Tuesday! I need time to reflect. Mondays always come with the pressure of being a due date, which I thought I had come to terms with and overcame, but this class has reignited the silly Master course anticipation of the impending Monday. On a silly note, we should encourage a UoP student horror film and name it “Monday.” I’d buy it.
So, I will take some time to be honest about my struggles with inferential statistics. The explanations for statistical data output is fascinating for me and challenging to me. I spent a lot of time this week studying with Khan Academy. I have learned that as a researcher, there are more meanings to the equations and data output. “Sal,” the voice of Khan, showed entire statistical sentences being created with new symbols. I feel like I came into the class understanding the foundation of statistics and halfway through realizing that I am learning a new language entirely. I want to understand the language. I want to be confident in the use of the language. This week, I feel more like a fish out of water.
Overall, I am ready to master SPSS. That seems like a theme for me and RES 710.
Well, I don’t want to complain or sound lost for my reflection. So, I have learned so much this week. When deciding about a Null hypothesis for both the z-test and the t-test, it is important to remember the factors that affect the decision and remember that to an extent. They are within the control of the researcher.
Tokunaga (2016) reminded me that the three factors affecting the null hypothesis's decision are sample size, alpha, and the directionality of the alternative hypothesis. When the sample size is greater, there is a higher chance of rejecting the null hypothesis (Tokunaga, 2016). The larger the value of alpha, the higher the chance of rejecting the null hypothesis. When the alternative hypothesis is directional, there is a higher chance of rejecting the null hypothesis than a two-tailed (Tokunaga, 2016).
Tokunaga, H. (2016). Fundamental statistics for the social and behavioral sciences. Thousand Oaks, CA: Sage