# What is the Welch's t-test?

The Welch's t-test is a parametric statistical analysis method designed to compare the means of two independent groups, particularly useful when these groups do not share equal variances. Unlike the traditional Student's t-test, which assumes equal variances, the Welch's test provides a more reliable means of analysis under conditions of variance heterogeneity, enhancing the accuracy of conclusions drawn from empirical data.

# When should I use it?

The Welch's t-test should be employed when the assumption of equal variances between the two groups being compared is violated. This scenario is common in real-world data where natural variations within each group can lead to significant differences in variance. It is particularly applicable in clinical research, psychology, and other fields where data sets may not meet the stringent assumptions required by the Student's t-test.

# How to use it on EasyMedStat?

- Navigate to Statistics > Test variables within the EasyMedStat platform.
- Select the numeric variable you wish to compare across two groups.
- Choose a Yes-No or List variable to define these groups. If using a List variable, it should have exactly 2 modalities (the Welch's t-test is used to compare 2 groups)
- If the Welch's t-test is recommended, proceed by selecting "Compare values of ..." to initiate the analysis.

# Can I use another test instead?

While the Welch's t-test is tailored for scenarios with unequal variances, EasyMedStat may suggest alternative tests based on your specific data characteristics. For instance, if data distribution and variance criteria are met, the Student's t-test might be a viable alternative. The platform's intelligent algorithm ensures the most statistically valid test is applied to your data, guiding you towards accurate and meaningful results.

In the context of choosing an alternative to the Welch's t-test, the Mann-Whitney test serves as a non-parametric option that can be considered when the data distributions do not follow a normal distribution. This test is especially useful for analyzing rank-based differences between two independent samples, offering a robust alternative without the need for normality or equal variance assumptions.

# See also

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