Please write a response to each discussion post.
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In an effort to analyze results and determine if a difference exists among groups of a study, a researcher might use various test to assist in examination of data (Erford, 2014). There are 4 various test used to determine whether there are differences among groups. Each test is used from the same type of equation yet they produce different results about statistical testing. (Erford, 2014)
z-Test are used to pinpoint “…significant difference between means of a sample and means of a population”(Erford, 2014). If I wanted to know the mean number of students receiving financial aid at my organization versus those at other HBCUs, I could use this z-Test to determine if there was a significant difference in our students versus the overall average of other colleges.
t-Test are used to consider whether the is “…a difference between two sample means”(Erford, 2014). This particular type of test can be independent or dependent. Independent t-Test are comparisons of those means that are not necessarily “…connected to the same sample of participants”(Erford, 2014). The dependent t-Test is just the opposite and does compare means from the same sample (Erford, 2014).
ANOVA answers if there is a difference among several means. This type of test is used when “…two or more independent variables are examined across a single dependent variable” (Erford, 2014).
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t test– is a statistical test run to determine whether a statistically significant difference exists between two independent sample means.
Example: Measuring the average diameter of shafts from a certain machine when you have a small sample.
Dependent t test– like a t test, a dependent t test also compares two means but in this case the means are compared from the same sample of participants across time, such as with a pretest and post test administered to the same group.
Example– A gym teacher gives you a physical exercise test in the beginning of the year. Throughout the year you work on improving your score. At the end of the year you retest.
z-test– is used to compare a sample mean to a previously known population mean. It identifies whether there is a statistically significant difference between means of the sample and the population.
Example: The teacher wants to compare students in her math class (the sample) to students in other math classes throughout the district (the population). The teacher will administer a standardized test with a given mean and standard deviation. The sample is greater than 30.
ANOVA– is an analysis of variance is a collection of statistical models used to analyze the differences among group means and their associated procedures.
Example: A researcher wishes to know whether different pacing strategies affect the time to complete a marathon. The researcher randomly assigns a group of volunteers to either a group that (1) starts slow and then increases their speed, (2) starts fast and slows down or (3) runs at a steady pace throughout. The time to complete the marathon is the outcome (dependent) variable.
ANCOVA– is used to nullify the effects of a confounding variable by statistically removing the variability in the dependent variable caused by the confounding variable.
Example: pretest-posttest randomized experimental design, in which pretest scores are statistically controlled. In this case, the dependent variable is the posttest scores, the independent variable is the experimental/comparison group status, and the covariate is the pretest scores.