nonparametrisk

Nonparametrisk, often translated as nonparametric, refers to statistical methods that do not rely on assumptions about the underlying distribution of the population data. Unlike parametric methods, which assume data follows a specific distribution (like the normal distribution), nonparametric methods are more flexible and can be applied to a wider range of data types, including ordinal and nominal data. This makes them particularly useful when the distribution of the data is unknown, suspected to be non-normal, or when dealing with small sample sizes.

Key characteristics of nonparametric methods include their robustness to outliers and their ability to handle skewed data. They often work by ranking the data or by directly comparing observed frequencies to expected frequencies. Common nonparametric tests include the Mann-Whitney U test (an alternative to the independent samples t-test), the Wilcoxon signed-rank test (an alternative to the paired samples t-test), and the Kruskal-Wallis test (an alternative to one-way ANOVA).

While nonparametric methods offer flexibility, they can sometimes be less powerful than their parametric counterparts when the parametric assumptions are met. This means that if the data truly follows a normal distribution, a parametric test might be more likely to detect a significant effect if one exists. However, when those assumptions are violated, nonparametric methods often provide more reliable results. The choice between parametric and nonparametric approaches depends on the nature of the data and the research question being addressed.