QNT 351 Week 4 Reflection

Week Four Reflection
Grace Fuller, Akil Joseph, Gina Moore, James Ray
University of Phoenix
QNT/351
Instructor: Eugene Hewett
04/06/2015

Week Four Reflection
In this reflection Learning Team A will discuss the steps in testing a research hypothesis, comparing means of two or more groups, and calculating that correlation between two variables. Learning Team A will also discuss which topics the team members excelled in as well as which topics were more difficult for team members. Lastly, Learning Team A will discuss how the weekly topics relate to application in each team member’s individual career field.
The Steps in Testing a Research Hypothesis Quantitative research is conducted as an attempt to answer a research question or chosen hypothesis. One technique for assessing this research question is through a methodology called hypothesis testing. There are five basic steps to hypothesis testing. Learning these five essential steps will provide a methodology that can be utilized no matter of the problem at hand. The first step to testing a research hypothesis is to define the research hypothesis itself and make assumptions. Throughout hypothesis testing assumptions are made about the measurement level of the variable, the sampling method, the state of the populace circulation, and the specimen size.
The second step is to state the research and null hypotheses as well as to define the alpha. A research hypothesis must state the relationship in which one is truly intrigued about. Generally, research hypotheses are stated in terms of populace parameters in light of the fact that we need to utilize sample statistics to gauge populace parameters. A null hypothesis dependably negates the research hypothesis, typically expressing that there is no distinction between the populace mean and some predetermined quality. Setting the alpha allows the null hypothesis to be rejected in the event that the probability of the obtained Z was less than or equivalent to the determined alpha.
The third step to testing a research hypothesis is to select the sampling distribution and specify the test statistic. Generally, normal distribution and the Z statistic are utilized as the test measurement. This is not always the situation; however, one would utilize these steps regardless of the fact that he/she is utilizing an alternate distribution and test measurement.
The fourth step to testing a research hypothesis is to compute the test statistic. The z‐score is one sort of test measurement that is utilized to focus the likelihood of acquiring a given worth. To test theories, one must choose ahead of time what number to use as a cutoff for whether the null hypothesis will be dismissed. This number is often called the critical or tabled worth in light of the fact that it is seen in a table. It represents to the level of likelihood that one will use to test the theory. On the off chance that the processed test measurement has a smaller likelihood than that of the critical worth, the null hypothesis will be dismissed.
The fifth and final step to testing a research hypothesis is to make a decision and interpret the results. The entire purpose of inferential statistics is to extrapolate from restricted information to make a general conclusion. "Descriptive Statistics" generally portrays information without coming to any general conclusions. Be that as it may the testing aspects of statistics are about coming to general conclusions by utilizing the restricted information.
Comparing the Means of Two or More Groups When comparing the means for two or more groups, one must express the null hypothesis. Arbitrary examples of every populace must be decided upon to demonstrate the formula in a working status. To get a more precise example a higher number of the populaces ought to be utilized. A T-test is utilized for comparison of two or more groups. A T-test inspects whether two samples are diverse and is generally utilized when the

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