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Peersresponse3of25240.docxRespond to at least two of your classmates’ postings. As you respond, consider whether you can offer additional ideas for how to reduce type 1 and type 2 errors. Given your classmates’ examples, are there ways to adapt them to be directional or non-directional and vice versa? Are there suggestions you can offer on clarifying the hypothesis?. I need 140 words of response to each discussion post. Please remember to use scholarly sources beyond the textbook when possible for all of your postings.
Hypothesis Testing
Peer response 1:I am interested in the research of the stigma associated with major depressive disorder (MDD). Specifically, I am going to research the influence of social standing on the perceived stigma of MDD. I plan to begin my research with the development of two hypotheses. The null hypothesis (H0) implies a suggested relationship between the studied variables (Privitera, 2016). I assume that personal annual income positively correlates with the perceived stigma of MDD. It might be possible due to the belief in the impact of money and the quality of living on depression. In turn, the alternative hypothesis (Ha) is a claim contradictory to H0 (Privitera, 2016). It implies the absence of a positive correlation between personal annual income and the perceived stigma of MDD. The given alternative hypothesis is non-directional, as it does not specify the direction of the relationship between variables.
The statistical analysis of the data might lead to type I and type II errors. Type I error is the rejection of a true null hypothesis (Privitera, 2016). In the provided example, it is the incorrect denial of the positive correlation between personal annual income and the perceived stigma of MDD. In turn, a type II error implies the approval of a false null hypothesis (Privitera, 2016). I will make this error when incorrectly admitting the positive correlation between personal annual income and the perceived stigma associated with depression. I can minimize type I and type II errors by choosing a smaller level of significance and a larger population sample.
The most challenging aspect of developing a hypothesis is its simple and precise formulation. An efficient hypothesis should be scientifically significant and easily understood by any reader (Oludeyi & Olajide, 2017). Also, it should not be too long and should not contain indefinite words and phrases. I often struggle with the correct formulation of the hypothesis and have to rewrite it several times.
References
Oludeyi, O. S., & Olajide, O. E. (2017). Readings in education, human and sustainable development. Ijebu Ode, Ogun State, Nigeria: Tai Solarin University of Education.
Privitera, G. J. (2016). Statistics for behavioural sciences. Thousand Oaks, CA: SAGE Publications.
Peer response 2: Subject: Community Gardens: Children who eat raw vegetables are healthier than those who eat cooked vegetables.
Research Question: Does eating cooked vegetables have more health benefits than eating raw vegetables?
Null hypothesis: Children who Eat Cooked vegetables are healthier.
Alternative hypothesis: Eating Raw vegetables have more health benefits.
The alternative hypothesis is directional which means eating raw vegetables has a direct effect on health statuses.
Type 1 error could exist on the chance that there were no effects on health from eating cooked vegetables. For example, our sample depends on the chance that it does not matter if carrots, tomatoes, kale, or peppers are cooked or raw when they are eaten. I mistakenly determined that no such variables existed which represents a false positive depending on how extreme the data is to reject the null hypothesis which in this case seems to be significant. Therefore, I must set the alpha level at .05 to avoid the type 1 error (Malec & Newman, 2013).
Type 11 error is under the power of the researcher to make sure the measurements are capturing the appropriate effects of eating vegetables raw or cooked and find out that it does not matter this could cause a problem in the prediction that one way is better than the other. Therefore, I must be careful coding and analyzing, be aware of small mistakes entering data, calculating totals, and choosing an inappropriate analysis (Malec & Newman, 2013).
I find the most challenging thing about hypothesis testing is analyzing, coding, and calculating statistics. I have always been apprehensive about mathematical equations so it will be difficult for me to understand the formulas used in null hypothesis testing using symbols and numbers.
Malec, T. & Newman, M. (2013). Research methods: Building a knowledge base. San Diego, CA: Bridgepoint Education, Inc.
