Description: The Mann-Whitney U Test is a non-parametric statistical technique used to compare two independent samples. Unlike parametric tests, which assume that data follows a normal distribution, this test does not require such assumptions, making it particularly useful in situations where data do not meet normality requirements. The test assesses whether there are significant differences between the distributions of the two samples, based on the ranks of the data rather than their absolute values. This allows the test to be robust against outliers and skewed distributions. The Mann-Whitney U is calculated by assigning ranks to the combined data from both samples and then comparing the sum of the ranks of one of the samples. If the sum of the ranks is significantly different from what would be expected under the null hypothesis (which posits that there is no difference between the samples), it can be concluded that there is a statistically significant difference between the two populations. This test is widely used in various disciplines, including psychology, medicine, and social sciences, where researchers often face data that do not meet the normality assumptions required by parametric tests.
History: The Mann-Whitney U Test was developed by American statisticians Henry Mann and Donald Whitney in 1947. Its creation arose in response to the need for statistical methods that did not rely on the assumption of normality of the data, which was a common challenge in empirical research. Since its introduction, the test has evolved and has been integrated into the arsenal of statistical tools used across various disciplines, becoming a standard in non-parametric analysis.
Uses: The Mann-Whitney U Test is primarily used in research where two independent groups are compared, and the data do not meet normality assumptions. It is common in psychology studies to compare scores from different treatments, in medicine to assess the effectiveness of two distinct treatments, and in social sciences to analyze differences in behaviors or attitudes between groups. It is also applied in market studies and survey analysis where data may be ordinal or not normally distributed.
Examples: A practical example of the Mann-Whitney U Test could be a study comparing anxiety levels between two groups of patients receiving different types of therapy. If data on anxiety levels are collected and found to be non-normally distributed, the test can determine if there are significant differences in the effectiveness of the therapies. Another example could be comparing customer satisfaction scores between two different products in a survey, where responses are ordinal.
