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
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 Tests like the Kolmogorov-Smirnov or Shapiro-Wilk check if
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. They ensure that whether a scientist is testing
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.