パラメトリック statistics is a branch of 統計的方法 that makes certain assumptions about the parameters of the population distribution from which the data is drawn. Commonly, these methods assume that the data follows a 正規分布, although other distributions may also be considered depending on the context.
In parametric statistics, parameters such as mean and variance are estimated from the sample data, and statistical tests are conducted based on these estimations. This approach is often contrasted with ノンパラメトリック統計, which do not assume any specific distribution and are used when such assumptions cannot be met.
パラメトリック統計手法の例には、t検定、 分散分析（ANOVA） (Analysis of Variance), and 線形回帰. These methods can provide powerful insights when the assumptions hold true, allowing for more precise estimates and conclusions regarding population parameters.
It’s important to note that the reliability of parametric statistical results heavily depends on the validity of the assumptions made. If the underlying data significantly deviate from the assumed distribution, the results can be misleading. Therefore, researchers often conduct tests for normality and other assumptions before applying parametric methods and may switch to non-parametric alternatives if the assumptions are violated.