Statistics is a branch of mathematics that involves collecting data and interpreting it. It includes a variety of tests. One example of a statistical test that is used to organize data is the one sample z-test. In this statistical test, there is only one group being tested. The one sample z-test is used to compare a sample statistic against a population Salkind (2014). It determines whether a sample can represent a population. The information provided by the one sample z-test gives enough information to make a conclusion of the sample group to the larger group. The z-test is an inferential test. The z-test is used to calculate a z value or the obtained value, which is then compared to a critical value. The information found from the z-test
…show more content…
It is used to study a group of subjects under two different conditions. The dependent t-test is useful for comparing the difference between a pretest and a posttest. Another instance where the independent t-test can be useful is before and after an experiment. Unlike the z-test the participants in the dependent t-test are tested more than once Salkind (2014). The dependent t-test is used to review differences between variables that are only tested once. There are only two groups in the independent t-test. One example to highlight how this t-test can be used is to observe the difference of a student before and after learning the material from a classroom setting. In the dependent t-test, the participants are being tested more than once.
The dependent t-test can be applied to the healthcare field. One example of the dependent t-test is when measuring blood pressure of a patient before and after giving insulin. Insulin is typically given to patients that have diabetes. The dependent t-test can measure the level of blood sugar in the body. The null hypothesis for the dependent t-test is that there is no difference between the two scores. The research hypothesis for the dependent t-test is that there is a difference between the two groups. …show more content…
In Central Hospital, in Daytona, Ohio there are many patients enrolled within the hospital’s weight loss program. The weight loss program has two different groups. The first group includes participants who exercise with aerobic exercise and the second group runs. The participants in the weight loss program are both female and male. The participants will range between the ages of eighteen and forty. The simple ANOVA can detect if there are differences among the differing groups, and genders. The participants are only tested once in this study. To begin conducting the factorial ANOVA, it is important to identify the null and research hypothesis. The null hypothesis is that there is no difference between the mean and there is no interaction. The research hypothesis is that there is a difference between mean and an interaction occurs. The level of risk is going to also be set to calculate the test statistic. An interaction can occur between hours of exercise and gender, such that women lose more weight running while men lose more weight with aerobic exercise. The factorial ANOVA is able to detect interaction, and differences within the groups and their
It is used to study a group of subjects under two different conditions. The dependent t-test is useful for comparing the difference between a pretest and a posttest. Another instance where the independent t-test can be useful is before and after an experiment. Unlike the z-test the participants in the dependent t-test are tested more than once Salkind (2014). The dependent t-test is used to review differences between variables that are only tested once. There are only two groups in the independent t-test. One example to highlight how this t-test can be used is to observe the difference of a student before and after learning the material from a classroom setting. In the dependent t-test, the participants are being tested more than once.
The dependent t-test can be applied to the healthcare field. One example of the dependent t-test is when measuring blood pressure of a patient before and after giving insulin. Insulin is typically given to patients that have diabetes. The dependent t-test can measure the level of blood sugar in the body. The null hypothesis for the dependent t-test is that there is no difference between the two scores. The research hypothesis for the dependent t-test is that there is a difference between the two groups. …show more content…
In Central Hospital, in Daytona, Ohio there are many patients enrolled within the hospital’s weight loss program. The weight loss program has two different groups. The first group includes participants who exercise with aerobic exercise and the second group runs. The participants in the weight loss program are both female and male. The participants will range between the ages of eighteen and forty. The simple ANOVA can detect if there are differences among the differing groups, and genders. The participants are only tested once in this study. To begin conducting the factorial ANOVA, it is important to identify the null and research hypothesis. The null hypothesis is that there is no difference between the mean and there is no interaction. The research hypothesis is that there is a difference between mean and an interaction occurs. The level of risk is going to also be set to calculate the test statistic. An interaction can occur between hours of exercise and gender, such that women lose more weight running while men lose more weight with aerobic exercise. The factorial ANOVA is able to detect interaction, and differences within the groups and their