In what scenario would you typically use a Wilcoxon test?

The Wilcoxon test is particularly useful in scenarios where the assumptions for normal distribution are violated, making it an appropriate choice for non-parametric data. Non-parametric tests, like the Wilcoxon, do not assume that the data follows a normal distribution and are designed to be more robust in dealing with data that may be skewed or ordinal. This characteristic enables researchers to analyze data that do not meet the stringent criteria required for parametric tests, such as the t-test, which assumes normally distributed populations and similar variances.

In studies where data cannot be assumed to be normally distributed, using the Wilcoxon test allows for valid statistical inferences without the risk of violating these assumptions. By utilizing ranks instead of raw scores, the Wilcoxon test provides a way to analyze differences between groups or conditions without the need for normality, which is essential for accurately interpreting the results in the presence of non-normal distributions. This makes it ideal for examining scenarios where the data is skewed or has outliers, thereby ensuring the integrity of the statistical conclusions drawn from the analysis.