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How Do I Perform a Two Sample t-Test in Excel Using SigmaXL?

Two Sample t-Test with Assumptions test

  1. Open Customer Data.xlsx, click Sheet 1 tab (or press F4 to activate last worksheet). We will look at comparing means of Customer Satisfaction by Customer Type ( 2 vs. 3), using the Two Sample t-test. H0: μ2=μ3, Ha: μ2≠μ3
  2. Click SigmaXL > Statistical Tools > 2 Sample t-test. If necessary, check Use Entire Data Table, click Next.
  3. With stacked column format checked, select Overall Satisfaction, click Numeric Data Variable Y >>; select Customer Type, click Group Category X >>; H0: Mean Diff = 0; Ha: Not Equal To; Confidence Level: 95%; ensure that Assume Equal Variances is checked:

  4. Two Sample t-Test

  5. Click OK. Select Customer Type 2 and 3.

  6. Comparison Factors

5. Click OK. Resulting output:

t-Test Results

Given the p-value of .0007 we reject H0 and conclude that Mean Customer Satisfaction is significantly different between Customer type 2 and 3. This confirms previous findings.

  • 2 Sample t-Test Assumptions Report:

  • This is a text report with color highlight: Green (OK), Yellow (Warning) and Red (Serious Violation).
    Each sample is tested for Normality using the Anderson Darling test. If not normal, the minimum sample size for robustness of the 2 sample t-Test is determined utilizing Monte Carlo regression equations (see Basic Statistical Templates – Minimum Sample Size for Robust t-Tests and ANOVA). If the sample size is inadequate, a warning is given and the Nonparametric Mann-Whitney Test is recommended.

  • Each sample is tested for Outliers defined as: Potential: Tukey's Boxplot (> Q3 + 1.5*IQR or < Q1 – 1.5*IQR); Likely: Tukey's Boxplot 2*IQR; Extreme: Tukey's Boxplot 3*IQR. If outliers are present, a warning is given and recommendation to review the data with a Boxplot and Normal Probability Plot. Here we have a potential outlier for Customer Type 2.

  • Tip: If the removal of outlier(s) result in an Anderson Darling P-Value that is > 0.1, a notice is given that excluding the outlier(s), the sample data are inherently normal.

  • Each sample is tested for Randomness using the Exact Nonparametric Runs Test. If the sample data is not random, a warning is given and recommendation to review the data with a Run Chart.

  • A test for Equal Variances is also applied. If all sample data are normal, the F-Test is utilized, otherwise Levene’s Test is used. If the variances are unequal and the test being used is the equal variance option, then a warning is given and Welch’s test is recommended.

    See Appendix Hypothesis Test Assumptions Report for further details.

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