100 Statistical Tests -

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

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. 100 Statistical Tests

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. The probability that the observed results occurred by chance

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 While the idea of "100 tests" may seem

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, statisticians group them based on the nature of the data and the specific question being asked: