# MASH : Maths and Stats Help

### Independent Samples t-Test

#### Introduction

An independent-samples t-test compares the means between two unrelated groups, such as comparing the difference between class 1 and class 2, another very common example would be used to compare a binary gender difference.

If you have more than three groups consider using a One-Way Between-Subjects ANOVA

It is considered a parametric test and is only suitable for parametric data. To check if your data is parametric, please read our dedicated guide: Parametric or Not Guide (PDF)

If your data is non-parametric you should consider using a Mann-Whitney U-Test.

#### Test Procedure

1. Click Analyze > Compare Means > Independent-Samples T Test 2. Within the Independent-Samples T Test box, select the test variable or dependent variable you are analysing and move it to the test variable box. Then move your independent/grouping variable into the “Grouping Variable” box. 3. Select Define Groups and specify the groups you are going to test. In this example I am testing Class 1 against Class 2 therefore my groups are numbered 1 and 2. 4. Select Continue or OK

#### Results SPSS will generate three tables, for this test we need two, the Group Statistics and the Independent Samples Test:

##### Group Statistics

This table shows a selection of descriptive statistics: the sample size of each group (N), the mean of each group (Mean), and the standard deviation of each group (Std. Deviation), best practice is to report them all.

##### Independent Samples Test

This table shows the specific test results including the t-statistic (t), the degrees of freedom (df) the two-tailed significance or p-value (Two-Sided p), and the 95% Confidence Interval (95% Confidence Interval of the Difference).

## Reporting the Results in APA Formatting

English scores of class 1 and class 2 students were compared. On average, class 1 students (Mean = 6.00, SD = 1.77) scored higher than class 2 students (Mean = 5.00, SD = 2.62). An independent-samples t-test indicated this difference, 1.00, 95%CI [-1.40, 3.40] was not statistically significant, t(19) = 0.89, p=.386