This post has been written by Jigsaw Faculty Kafeel Basha

Let me begin this article by explaining exactly what a t- test is. I will then explain when we can use the t- test and then go on to tell you all about the procedure we can use to perform the t- test using the language of SAS.

Well, a t-test is a statistical significance that indicates whether or not the difference between two sample means, most likely reflects a real difference in the population from which the groups were sampled. In simple words, t- test is used to find the mean difference between population mean and sample mean.

**When can we apply t- test?**

We can apply the t- test when we have a sample size n<30, population mean, sample mean and sample standard deviation.

**Procedure used to perform t- test in sas: Proc ttest**

Type t- test using SAS

SAS Code for t- test: One sample

Syntax

proc ttest data=name;

var <Option>

run;

Where “name” is the data set name used for the test and “<Option>” gives the variables used for the t test ie “option=variable name” where the sample data is stored. Each variable that was listed on the **var** statement will have its own line in this part of the output. If a **var** statement is not specified, **proc ttest** will conduct a t-test on all numerical variables in the dataset.

SAS Code for t- test: Two sample (Independent Sample)

Syntax

proc ttest data=name;

class <Option>;

var <Option>;

run;

Here class statement subgroup the two different sample and we have to specify the variable name where the observation for both the samples are stored.

Note: We have to arrange the data before performing two sample test.

(Example given, Where X and Y are two samples for T test)

SAS Codes for T test: Paired (Dependent Sample)

Syntax

proc ttest data=name;

Paired v1*v2;

run;

Where v1 and v2 are the dependent variables.

Output of T test:

- This first table provides means, standard deviation, min and max for each group and the mean difference.
- The next table provides 95% Confidence Limits on both the means and Standard Deviations, and the mean difference using both the pooled (assume variances are equal) and Satterthwaite (assume variances are not equal) methods.
- Before deciding the appropriate ttest we should look for if the variances for the two groups are equal. If the p-value (Pr>F) is less than 0.05, you should assume UNEQUAL VARIANCES.
- Choose the Satterthwaite t-test, if we have unequal variance. If the variances were assumed equal, you would report the Pooled variances t-test.
- The last step is according to p value we get, we accept or reject the null.
- When you use the ODS GRAPHICS ON; option, we get graph which provides a visual comparison of the means (and distributions) of the two groups.
- Also we can see why the test for equality of variances equal or unequal.

Note: For paired t test Pooled and Satterthwaite methods are not required as the variables are dependent.

Related Articles:

Logistic Regression in SAS

5 Popular Tools for Data Visualization

Understanding Dummy Variable Traps in Regression

Popular Applications of Linear Regression for Businesses

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