A t-test is a fundamental statistical analysis, often employed in hypothesis testing, to determine if there is a statistically significant difference between the means of two groups. The sources indicate that it is a staple analysis frequently used in data analysis due to its utility and relative simplicity.
There are primarily three types of t-tests:
- Independent t-test (or unpaired t-test): This test is used to compare the means of two groups that are independent of each other. For example, comparing the sales of two different products or the performance of two unrelated groups.
- Dependent t-test (or paired t-test): This test is used when the two groups being compared are inherently related or paired. This often involves measuring the same group at two different time points, such as comparing a group’s performance before and after an intervention.
- One-sample t-test: This test compares the mean of a single group against a known or hypothesized single value. For instance, comparing the average test scores of a class to the national average.
While a t-test technically calculates a t-score, in practice, data analytics tools typically provide the p-value as a key output. This p-value is crucial for hypothesis testing, as it helps determine whether to accept or reject the null hypothesis based on a chosen significance level (alpha). A low p-value (typically less than or equal to alpha) suggests that the observed difference between the group means is statistically significant and not likely due to random chance.
The sources emphasize that for the exam, it’s important to understand when to use a t-test: specifically, when you need to compare two groups that contain quantitative data. The independent variable separating the groups is usually categorical, while the variable being measured and compared (the dependent variable) is numerical. It is also mentioned that while there are assumptions for t-tests, such as normality and homogeneity of variance, you won’t be asked to perform a t-test or deeply assess these assumptions on the exam, but rather understand the concept and its application.