# Generating Hypotheses for t-Tests and ANOVAS

| June 19, 2015

Generating Hypotheses for t-Tests and ANOVAS

Post a null hypothesis that would use a t test statistical analysis. Use the same hypothetical situation taken in the t test hypothesis, and turn it into a null hypothesis using a one-way ANOVA analysis and a two-way ANOVA. Be sure to justify your answer. Cite Reference.
Here is the hypothetical situation taken in the t test hypothesis (Also complete by Best Essay):
A psychological research question which can be answered by z test, t test for independent samples and t test for dependent samples is the impact of hate crimes on victims and communities. There are a number of circumstances in which hate crimes affect individuals as well as the communities at large. These statistical methods can be noted as the equivalent; this is due to the fact that two means are being compared in order to propose if both samples originated from similar populations. These statistical methods use samples that have distinctions in variances. For the case of the effect of hate crimes on victims and communities, the entire approach can be utilized which will provide a good justification for the reasons behind the impact of hate crime (Howell, 2011).
To determine the impact of hate crimes on communities along with victims, the data collected for the t test to a large extent should come independent from each other. This is applicable when the sample selected for the population is different. The random sampling selected in response to the impact of hate crimes on victims and communities may reveal diverse opinions in relation to what is consider as the result of crime or not. To assess the differences between a set of victims and communities facing these effects such as crime leading to corruption in the community both t test and z test is utilized (Howell, 2011). This is because the probability in which the responses will be revealed results in critical decision making. A substantial number of measurements will be put into place which can be used to thoroughly reveal the impacts that are observable in the community. Since the sample chosen will depend on the different size of population including gender, age and population sample, these statistical methods offer additional insights to establish the relationship between hate crime and communities.
Reference:
Howell, D. C. (2011). Statistical methods for psychology. Boston: Cengage Learning.
Below is an example of what my Professor is looking for to get a 100 on this assignment.
Random selection is the method for selecting a sample that uses truly random procedures; usually meaning that each person in the population has an equal chance of being selected (Aron, Aron &amp; Coups, 2008, p.87). Though the aspect of using random selection in subjects is extremely complex, psychologists try to achieve this by arbitrarily picking subjects drawn from the targeted population without biases (Howell, 2011 p.3).
A researcher can make a list of students by recording their attributes and names, then picking their names from a pool of small pieces of papers with names placed in a large bucket and picking one name at a time and reshuffling them again before picking the preceding name (Howell, 2011 p.3). This process when done manually can be tedious and cumbersome especially if the population is large. However, if the process is automated using specialized software such as statistical product and service solutions (SPSS), statistical analysis system (SAS) or statistics and data (STATA) it can be less complex.
Typically the applying of probability is to represent the chances a subject has to being selected compared to the rest of the subjects (Howell, 2011 p. 109). The following examples can be used to demonstrate the meaning of probability. Since there is a total of 50 students seeking counseling center services, the probability of picking another file that falls under mental health issues would be 25/50 which would be represented on a scale of 0 to 1 as 0.5, where 0 represent no chances while 1 represents no comparison at all. The probability of picking another file that falls under learning/school issues would be 15/ 50 or 0.3; while picking another file that falls under relationship issues would be 5/50 or 0.1. Finally, the probability of picking another file that falls in any category except other would be 45/50 or 0.9.
In closing, the probabilities and results would be different using convenience sampling; since we would not be obliged to have a list of all the files in those categories. This would mean using the most accessible files that are quick to pick; the first five files from mental health issues could be picked creating a significant bias that would not be a true representative of the entire population.

References
Aron, Aron, Coups, A. (2008). Statistics for Psychology (5th ed). Pearson Learning Solutions. Retrieved August 3, 2013 from http://digitalbookshelf.argosy.edu/books/0558403867/id/ch02lev2sec10
Howell, D. C. (2011). Statistical methods for psychology. Cengage Learning.

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