Type I Error Paper

Analysis of variance (ANOVA) is an inferential procedure used to test a hypothesis by evaluating the mean differences between two or more treatment conditions (Gravetter & Wallnau, 2014). Since ANOVA can have multiple variables, independent and quasi-independent variable are called factors; and the various conditions are called levels of the factor (Gravetter & Wallnau, 2014). Therefore, researchers would use ANOVA when there are multiple factors. ANOVA can basically be used anytime a t-test would be used, but can also be used by researchers when there are more than

Tony’s Chips has been acquired by a new independent company. The new company’s intention is to focus on the e-commerce website that they have planned. Currently, the old website is externally hosted and it is required to be moved to an internal one. This new system will store, retrieve, and have data recovery solutions for the company. An additional backup site will need to be created

A one-way variance ANOVA analysis consists of determining the differences between independent, unrelated groups. Furthermore, the ANOVA analysis can also define the statistically significant of the mean. By comparing the mean between all groups involved this will lead to the results of the research, and now the researcher can test the null hypothesis. Nevertheless, the ANOVA also includes a post hoc test. A post hoc test purpose is to locate and confirm that among each group a difference occurred.

17. Write answers in paragraphs in response to two of the following questions. Each answer should be approximately 200 words. Support your answer with specific references to Still Stands the House. Organize your ideas to express them clearly and coherently.

Indianapolis has a lot of things to do. The zoo is fairly close to the campus; they have the Indianapolis 500 yearly, there are malls, museums,and concerts within the city. IUPUI is located in Indianapolis, IN. Indiana University Purdue University Indianapolis was founded in 1891, Indiana and Purdue merged in 1969. Their mascot is the Jaguar. They have several sports, women's basketball, men's basketball, women’s volleyball, etc.

It is a pretty tricky to assume that smaller the alpha would give better results. Unfortunately, it does not work that way. It all comes down with how much one is willing to take the risk, how meaningful it is. If a researcher set a lower alpha but get a higher one, then what? If the researcher accepts the low alpha while it was a higher alpha then he makes a type II error. Another case would be when some researcher reports a low alpha despite many studies shows higher alpha using the same method.

A two-sample t-test is a hypothesis test that is used to compare if there is a difference between two groups. One of the first steps in a two-sample t-test is to establish a hypothesis. The two-sample test helps to answer hypotheses that question how the results of one group that may already be in place compare to the results of another group that is new. The two-sample hypothesis test is a common hypothesis test used in many industries.

Selecting 500 high school athletes around the OKC area represents a small sample of high school athletes in Oklahoma as a whole. If I conducted a t Test and it yields a low probability that the null hypothesis is correct, I will reject the null hypothesis. If I compute and discover a large variance, this will result in a low enough probability to allow rejection of the null hypothesis as well. Before reporting the results of a t Test, I must report the values of the means and also the values of the standard deviations and the number of cases in each group. If I reject the null hypothesis, I will be declaring statistical significance. It is possible that I can find statistical significance, but not practical significance. If I conclude that p is greater than .05 from my data, I unknowingly made a Type II Error because I didn’t reject the null hypothesis, which incorrectly states that there is no difference between the population means. I don’t need to further check for sampling error if my data is independent because there is no pairing or matching of individuals across the two samples. It is necessary to report if my data is independent or dependent because it may have less sampling

ANOVA allows statistic practitioners to determine whether to reject the null hypothesis or accept the alternate hypothesis. Analysis of variance tests can be classified as one-way ANOVA or two-way ANOVA. The differences between these classifications depend on the number of independent variables in the testing process. The one-way ANOVA testing has one independent

Rationale: During viral infection, most viral pattern recognition receptors (PRRs) stimulate the expression of type I interferons (IFNs), which are the most potent known antiviral factors and are capable of limiting the replication and spread of most viruses (71-73). Type I IFN not only plays an essential role in defense against papillomavirus infection at any stage of the virus life cycle, it also regulates multiple aspects of innate and adaptive immunity (74, 75). Papillomaviruses evade immune surveillance by downregulating the expression of IFNs in host cells (61-63). For example, HPV16 E6 and E7 proteins are found to downregulate the expression

My observation of Standard 4 is that an overseer promotes student achievement by collaboratively functioning with all stakeholders and responding to their varied needs and interests. The most up to date brain study and knowledge tell us that all students learn differently, have personal strengths, weaknesses and desires. One way we do this at Hawthorne Elem is through our Care Team and IEP Checklist Process. During such meetings, a group of educators gather to recommend individualized plans of interventions and modifications to particularly help the victory of a student. This collaboration procedure mandates that students pull together the necessary assets to ensure their success.

Moreover, we utilize the “Post Hoc test which tests every possible pair which is essentially performing a t-test for each pair” (Mirabella, 2011, p. 5-7). The vital information contained within the ANOVA table has a considerable amount of information however, the most critical portion of the table is the p-Value that is acquired from it. Based on Lane (2013, p. 1) indicates that, “Analysis of variance can be used to test differences among several means for significance without increasing the Type I error rate.”

When there are statistically significant differences between the independent variable Bonferroni post hoc tests was used to determine where the difference lies in the Pairwise Comparison table. The following are the results of my study.

Whenever his army marched into a land, it was not just an army of warriors; he brought an entourage of scholars to spread the culture as well. Interspersed within the fighting battalions were architects, philosophers, scribes, musicians, and educators whose whole duty was to spread the culture to those who were conquered. Some of these strategies were the establishment of Greek cities, Greek schools (Gymnasia), and commanding the Greek language as the official language for government and the de facto language for commerce. Many people began to give their children Greek names, and local styles of art and architecture began to imitate Greek models. Even some of the Jewish High Priests took Greek names. All of this was not a good turn of events for Israel since the Greek culture was very ungodly and humanistic. Despite the bright and promising future ahead of him, Alexander met an unexpected death in Babylon at the age of 32 and though one will like to believe that all that he stood for will now be in vain, the case was not so but rather what he initiated had only just begun. After the death of Alexander his field marshals struggled for dominion of the lands they had conquered.

A Type I error is the worst type of error a researcher can make while conducting an experiment. A Type I error is the rejection of the null hypothesis when the null hypothesis is actually true. Therefore, a Type I error is a false positive and it indicates that results are significant when they are not. For example, if a researcher states that the amount of antidepressants taken by individuals will decrease the number of suicides, the outcome could be potentially harmful. Individuals would become reliant on the medication and have expectations of the antidepressant lowering their likelihood of committing suicide. Unfortunately, this Type I error could potentially increase suicide. Setting a low alpha level will allow the researcher to avoid