Question

In which situation does the df error and df treatment apply?

A) Paired t-test with non-pooled data
B) Two independent sample t-test with pooled data
C) ANOVA with more than two groups
D) Chi-squared test for unpaired categorical data

Answer 1

Option (C), The df error and df treatment are relevant in the context of a one-way ANOVA, which is used to test whether multiple group means are equal when dealing with more than two independent groups.

Step-by-step explanation:

The situations where the terms df error and df treatment apply are in the context of Analysis of Variance (ANOVA), specifically one-way ANOVA. These terms are involved in testing whether multiple group means are equal in situations where you have more than two independent groups. The 'df error' refers to the degrees of freedom associated with the variability within the groups (error term), while 'df treatment' pertains to the degrees of freedom associated with the variability between the group means (treatment effect).

In ANOVA, the df error is calculated as the total number of observations across all groups minus the number of groups, and the df treatment is calculated as the number of groups minus one. The F-statistic used in ANOVA is computed using these two degrees of freedom.

Based on the examples provided, the correct answer is option C, which states 'ANOVA with more than two groups'. This is the scenario where both df error and df treatment are relevant, as they are part of the calculations for the F-statistic in the ANOVA test.