Measuring of the project outcomes.
When conducting a t-test, the researcher is looking for an indication of the differences between two groups or conditions. Specifically, the researcher wants to determine if there is a statistically significant difference in the mean scores between two groups or conditions. Descriptive statistics such as means and standard deviations are used to describe the data that has been collected from each group or condition being studied. In addition, inferential statistics such as t-tests are used to compare these descriptive statistics across groups/conditions and draw conclusions about differences between them.
The results of the t-test indicate whether or not those differences are statistically significant at a given level of confidence (typically 95%). If this confidence level is met, then it can be assumed that there is a significant difference in means between two conditions; if not, then it can be concluded that no significant difference exists and therefore any observed differences may have resulted by chance alone.
In order to determine what type of t-test should be conducted (i.e., one sample vs paired samples), researchers must first decide whether they would like to compare one group against some fixed value (one sample) or if they would like to compare scores on some dependent variable for different groups before and after treatment (paired samples). The appropriate test will depend on research goals and design considerations.
A key part of interpreting t-test results includes examining effect size through reporting measures like Cohen’s d which describes how large a difference exists between two means relative to their variability. Effect size provides information about practical significance rather than just statistical significance which helps ensure meaningful interpretations of results in real world contexts outside of academic settings. For example, even though there may not be a statistically significant relationship found through traditional testing methods – effect sizes can offer insight into how strong relationships actually exist within populations so that estimated effects still inform decision making processes related to interventions deemed “not significantly different” when analyzed with traditional tests alone.