100 Statistical Tests 【Essential】

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

The probability that the observed results occurred by chance. Generally, a p-value less than 0.05 suggests the result is "statistically significant." Choosing the Right Tool 100 Statistical Tests

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. Tests like the Kolmogorov-Smirnov or Shapiro-Wilk check if

These are the workhorses of research. A One-sample t-test compares a group to a known value, while an Independent samples t-test compares two distinct groups. For three or more groups, the F-test (ANOVA) is used. Non-parametric tests (like the Mann-Whitney U or Wilcoxon

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

While the idea of "100 tests" may seem overwhelming, they represent a refined evolution of logic. They ensure that whether a scientist is testing a new life-saving drug or a marketer is testing a website layout, the conclusions drawn are rooted in mathematical probability rather than intuition.