INDEPENDENT SAMPLES T-TEST REPORT ANALYSIS

Independent Samples t-test Report Analysis



Independent Samples t-test Report Analysis

Data File Description

The data file is based on the demographics such as age, Gender, IQ etc. The t-test assesses if the means of two groups are statistically distinct from each other. This analysis is appropriate when you want to compare the means of two groups, and especially appropriate as the analysis for the posttest-only two-group randomized experimental design.

Figure 1. Idealized distributions for treated and comparison assembly posttest values.

Figure 1 shows the distributions for the treated (blue) and command (green) groups in a study. Actually, the number shows the idealized distribution -- the actual distribution would usually be depicted with a histogram or bar graph. The number indicates where the command and treatment assembly means are located. The question the t-test addresses is if the means are statistically different.

Assumptions, Data Screening, and Verification of Assumptions

Bivariate unaligned variable (A, B groups)

Continuous dependent variable

Each fact of the reliant variable is inreliant of the other facts of the dependent variable (its likelihood circulation isn't influenced by their values). Exception: For the paired t-test, we only need that the pair-differences (Ai - Bi) be unaligned from each other (across i). [Note: unaligned" and reliant" are utilised in two distinct senses here. Just think of a "dependent variable" as one thing, and "observations that are dependent" as another thing.]

Dependent variable has a normal distribution, with the same variance, s2, in each group (as though the distribution for group A were merely shifted over to become the distribution for group B, without changing shape):

This leads us to a very important conclusion: when we are looking at the differences between scores for two groups, we have to referee the distinction between their means relative to the spread or variability of their scores. The t-test does just this.

Inferential Procedure, Hypotheses, Alpha Level

The equation for the t-test is a ratio. The peak part of the ratio is just the distinction between the two means or averages. The base part is a measure of the variability or dispersion of the scores. This formula is essentially another example of the signal-to-noise metaphor in research: the distinction between the means is the signal that, in this case, we believe our program or treatment presented into the data; the base part of the formula is a measure of variability that is essentially noise that may make it harder to see ...