Statistic Analysis : T Test

We conduct an independent sample t-test using Excel, and obtain the following output (see t-test-height)

You are a patrol captain attending a staff meeting. An analyst that works for your department is presenting the results of a study intended to determine which of four separate patrol strategies are the most effective at reducing speeding around schools.

Hunsaker et al. (2015) clearly discussed the statistical tests that were used in the study. The data was analyzed by the Statistical Package for Social Science, a series of Pearson r correlation, t test and a one-way analysis of variance. The results of the study were sufficiently presented in Table 1, which used the Stepwise solution for analysis.

The analysis included t-tests (Studies III & IV), Pearson correlation, multiple regression (Studies I & III), and repeated measures ANOVAs (Study IV).

While the correlational data in this study is wonderful, the remaining parts of the data are quite confusing. Many of the statistics just do not make any sense. In

A t-test asks whether a difference between two groups’ averages is unlikely to have occurred because of random chance in sample selection. A difference is more likely

This non-experimental research uses descriptive statistical analysis, paired t-tests, and analysis of variance to examine the study variables.

The author uses the statistical package (SPSS v.16) in order to compare and analyze data of the control and the experimental groups. The statistical tests described in the Method section or Data analysis paragraph but in the Result section.

Earthworms exist everywhere we go, but not everyone knows how they function and the reason they are helpful. I am interested in studying how Earthworms adapted to it’s environment as well as how they benefit the environment around it.

Quantitative data was represented as mean, standard deviation, median and range. Data was analyzed using student t-test to compare means of two groups and paired t-test compared pre and post results. Qualitative data was presented as number and percentage and compared using either Chi square test or fisher exact test. Graphs were produced by using Excel or STATA program. P value was considered significant if it was less than 0.05.

Since this study lacked statistical power in determining significant correlations due our small sample size (N = 62), we emphasize the effect sizes of our results using Cohen’s d. We utilized independent samples t-tests

1. The null hypothesis reveals the difference of data between 2010 and 2012, is consistent for all the math equations. H0: Mu = 119,155 2. The data from the readers in 2012, reveals that the alternative hypothesis is greater than 119,155, due to the difference in readers from 2010.

The results of this study are presented in the order the hypotheses were tested. Analyses of covariance were performed on all hypotheses and a level of significance of 0.05 was established as a criterion for either accepting or rejecting the hypotheses.

​A t-test was conducted in order to ascertain the t-distribution of participants under the null hypothesis in order to determine if the data sets vary significantly from one another. To note, the age range of the participants involved in this research ranged from individuals between the ages of 17 into their late 50s. The huge range of ages among the participants provide a workable foundation on how age is observed to have a distinct form of impact on how each person responds to the experiment and make choices according to direction and inference. While randomization is evident within the first set of responses to the first part of the experimentation, it could be understood how evidently effective the whole concept is particularly in

Statistical data were collected by using the Statistical Package of Social Sciences using a paired variance t-test (comparing before - after). An independent t - test was also used for comparison between the two groups (equal variance). The results were reported as mean ± SD.