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.
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. 100 Statistical Tests
The landscape of statistical analysis is defined by a vast toolkit of tests, often cited in the classic compendium 100 Statistical Tests by Gopal K. Kanji. These tests serve as the bridge between raw data and scientific certainty, allowing researchers to determine if their findings represent genuine patterns or mere coincidences. The Categorization of Tests Regardless of which of the 100 tests is
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. They ensure that whether a scientist is testing