A statistical method assesses if one treatment consistently yields higher results than another when applied to matched pairs. It analyzes the direction (positive or negative) of the differences within each pair, focusing specifically on whether the positive differences significantly outweigh the negative ones. For instance, consider a study comparing a new weight loss drug to a placebo. Each participant receives both treatments at different times. The test determines if the new drug leads to weight loss more often than the placebo, concentrating on scenarios where the weight loss with the drug exceeds the weight loss with the placebo.
This approach is valuable because it is non-parametric, meaning it doesn’t require the data to follow a normal distribution, making it suitable for various types of data. Its simplicity allows for easy understanding and implementation. Historically, it provided a readily accessible method for comparing paired observations before the widespread availability of complex statistical software. This test offers a robust way to determine if an intervention has a positive effect when dealing with paired data and non-normal distributions.
