In the context of an Unrelated T test, what must the data meet?

In the context of an Unrelated T test, also known as an independent samples T test, the data must meet parametric assumptions. These assumptions are crucial for the validity of the results from the test and include several key criteria:

1. Normal Distribution: The data in each group should be approximately normally distributed. This means that the data should follow a bell-shaped curve, which is significant for hypothesis testing because many statistical tests, including the T test, rely on the normality of the data.

2. Homogeneity of Variance: The variances in the two groups being compared should be roughly equal. This assumption ensures that the test can accurately compare the means without being affected by different levels of variability in the datasets.

3. Independence of Observations: The samples being compared must be independent of one another. This means that the selection or response of one participant must not influence the selection or response of another participant in any way.

Meeting these parametric assumptions allows the Independent T test to effectively determine if there is a statistically significant difference between the means of the two groups. If these assumptions are not met, the results of the test may not be reliable, leading to incorrect conclusions.