A T-test is a set of tests performed on statistical hypotheses to determine whether there is a significant difference between the means of two groups or samples (Kim, 2015). It is most commonly used when comparing the mean of two populations or a sample mean to a known population mean (Barret, 2018).
Types of T-tests:
According to Gerald (2018), there are three types of T-tests:
1. One-sample T-test: It is used to compare the mean of a sample to a known population mean.
Example: we could use a one-sample t-test to see if the average height of a group of students significantly differs from that of all people in the United States.
2. Independent samples T-test: It compares the means of two independent samples.
Example: we could use an independent samples t-test to see if the average GPA of students who took a specific study skills course is significantly different from the average GPA of students who did not take the course.
3. Paired samples T-test: It compares the means of two dependent samples.
Example: we could use a paired samples t-test to see if the average weight of a group of people before and after they went on a diet is significantly different.
The Impact of Levels of Measurement on T-test:
According to Williams (2021), the levels of measurement for the independent and dependent variables impact the appropriateness of using a t-test as an analytical procedure in the following ways:
- Nominal level variables can be classified into categories, but there is no inherent order to the categories. A t-test cannot be used to compare means for nominal-level variables because there is no way to order the categories.
- Ordinal level variables can be classified into categories, and the categories have a meaningful order. A t-test can be used to compare means for ordinal-level variables, but the results should be interpreted with caution because the difference between categories may not be equal.
- Interval level variables can be classified into categories, the categories have a meaningful order, and the difference between categories is equal. A t-test can be used to compare means for interval-level variables.
- Ratio-level variables are similar to interval-level variables, but the variable has a true zero point. A t-test can be used to compare means for ratio-level variables.
Conclusion
T-tests are a powerful tool for comparing means. In my research about construction pollution, the t-test results showed a significant difference in the levels of construction pollution between the two areas. The area with higher levels of construction pollution was also found to have a higher incidence of respiratory problems among residents. This suggests a link between construction pollution and respiratory health problems. These findings are significant because they provide evidence that construction pollution can hurt human health. This information can be used to advocate for stricter construction pollution regulations and educate the public about the dangers of this type of pollution.
References
Barret, S. (2018). Different types of t tests. [Video File]. Retrieved from https://www.youtube.com/watch?v=8_CmOXcHLSA (5:48)
Gerald, B. (2018). A brief review of independent, dependent and one sample t-test. International journal of applied
mathematics and theoretical physics, 4(2), 50-54.
Kim, T. K. (2015). T test as a parametric statistic. Korean journal of anesthesiology, 68(6), 540-546.
Williams, M. N. (2021). Levels of measurement and statistical analyses. Meta-Psychology, 5.