T-Test

It is paramount to understand the dependent and independent variables prior to discussing the research question. In broad terms, dependable variables can be described as the variable that depends entirely on other factors that are being measured. They are always expected to change due to results manipulation. They can also be described as the presumed effect. On the other hand, independent variables are stable and constant, and they are not affected by any other variables that are being measured (Akinwande, 2015). These are the systems in the experiment that are manipulated systematically by the researcher and can be described as the presumed cause.

Research Question

            Do men tally significantly higher in respect to math assignments than females?

With respect to T-Test, there are two hypotheses that can be attributed to the above research question.

H_o-male students do not score high in respect to math test more than females

H_a-males students score higher in respect to math tests than female students.

            Therefore, the basic idea behind this form of example during the calculation of t-test is first to find the main difference between the two means of the group and then divide it by the standard error of the difference between the two means, which is also the standard deviation of the distribution of the differences. During the calculation of the t-test, there can be two types of errors.

Type 1 error-this is when we reject the null hypothesis that is really true. The probability that one will choose a type one error mainly depends on the alpha level one chooses.

Type 11 error-this happens when one fails to reject a null hypothesis that is false. This happens when one tests and finds no difference between the two groups when there was one.

Independent and Dependent Variables

            The females’ and males’ scores in a math test can be described as the dependent variable in the test. The independent variable in the test can be the math test. The math test can be manipulated to test how the students will effectively score in different tests. For example, the teacher can decide to make changes in the test, which can be deemed as either difficult or easier with respect to the students’ understanding. This can help calculate the t-test and evaluate who scored higher between the two genders. As such, the teacher will be able to understand which of the two hypotheses is correct in respect to the t-test.

            The pair-difference t-test or t-test for dependent variable mainly measures the difference between the average tallies of a single individual sample who have been assessed in two different times (Hedberg, 2015). In our example, this can be the score of either boys or girls and be measured in two different times. The t-test for independent variables measures the difference between the averages of two samples. In our example, the procedure would be used to compare the results of both male and female students and compare whether the difference between the two groups is big enough to describe there is also a difference in each group.

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References

Akinwande, M. O. (2015). Variance inflation factor: as a condition for the inclusion of suppressor variable (s) in regression analysis. Open Journal of Statistics, 5(07), 754. doi:10.4236/ojs.2015.57075

Hedberg, E. C. (2015). The power of a paired t-test with a covariate. Social science research, 50, 277-291. doi:https://doi.org/10.1016/j.ssresearch.2014.12.004