Parametric tests are a type of statistical test that make specific assumptions about the parameters of the population distribution from which the samples are drawn. These tests typically assume that the data follows a 正規分布 そして、母集団の分散が等しいこと。
パラメトリック検定の一般的な例には、t検定、 分散分析（ANOVA） (分析 of Variance), and 回帰分析. These tests are often preferred because they can provide more powerful and precise results compared to non-parametric tests, especially when the assumptions are met.
パラメトリック検定の主な特徴は次のとおりです：
- 正規性の仮定： The data should be approximately normally distributed. This is particularly important for small sample sizes.
- 分散の均一性： The variances among groups should be similar. This is often tested using Levene’s test or Bartlett’s test.
- 区間または比率データ： Parametric tests typically require data measured on an interval or ratio scale, which allows for meaningful mathematical operations.
パラメトリック検定の仮定が満たされない場合、研究者は use non-parametric tests, which do not rely on these strict assumptions but may have less statistical power.
In summary, parametric tests are powerful statistical tools used to analyze data under specific conditions, making them a staple in many fields, including psychology, medicine, and 社会科学.