Runs t-test, which checks differences of means of variables between 2 groups.
Input data should contain following columns.
Target Variable - Numeric column whose means should be calculated and compared between groups.
Explanatory Variable(s) - Column with 2 different values to categorize rows into 2 groups.
Type of Alternative Hypothesis
Expected Mean or Difference in Means - Default is 0. Null hypothesis is that there are exactly this much difference between means of the 2 groups.
Paired - If TRUE, perform a paired t-test.
Assume Equal Variances - If TRUE, assume that variances are same between the 2 groups.
Confidence Level - Confidence level for confidence interval of difference of means between two groups reported in Summary Table.
Difference to Detect - Size of difference between means of 2 groups that is considered to be meaningful. Used to calculate effect size, Cohen's d. If not specified, mean difference calculated from the data is used.
Standard Deviation - Assumed standard deviation of the Target Value. Used to calculate effect size, Cohen's d. If not specified, calculated from the data.
Probability of Type 1 Error (P Value) - Probability of type 1 error that can be tolerated. Default is 0.05.
Probability of Type 2 Error (1 - Power) - (Optional) Probability of type 2 error that can be tolerated. If a value is specified, required sample size is estimated and reported in the Summary Table. If not, probability of type 2 error with current condition of the test is reported in the Summary Table.
Click Analytics View tab.
If necessary, click "+" button on the left of existing Analytics tabs, to create a new Analytics.
Select "T Test" for Type.
Select Target Variable column.
Select Explanatory Variable(s) columns.
Click Run button to run the analytics.
Select view type by clicking view type link to see each type of generated visualization.