100 Statistical Tests – No Login

To manage such a large number of procedures, statisticians group them based on the nature of the data and the specific question being asked:

The sheer volume of available tests exists because real-world data is messy. You might need a test for circular data (the ), a test for outliers (the Grubbs' test ), or a test for the equality of variances ( Levene's test ). Selecting the wrong test—such as using a parametric test on highly non-normal data—can lead to "Type I errors" (false positives) or "Type II errors" (false negatives). Conclusion 100 Statistical Tests

Tests like the Kolmogorov-Smirnov or Shapiro-Wilk check if a dataset fits a theoretical distribution, which is often a prerequisite for more complex modeling. The Logic of Hypothesis Testing To manage such a large number of procedures,

Parametric tests (like the t-test or ANOVA ) assume the data follows a specific distribution, usually the normal distribution. Non-parametric tests (like the Mann-Whitney U or Wilcoxon signed-rank ) make fewer assumptions and are used for skewed data or small samples. Regardless of which of the 100 tests is

Regardless of which of the 100 tests is used, they almost all follow a unified logic: The assumption that there is no effect or difference. The Alternative Hypothesis ( H1cap H sub 1 ): The claim that there is a significant effect.