P-values and its Interpretation in Permutation Tests
P-values in permutation testing indicate the likelihood that a test statistic observed under the null hypothesis will be as extreme as or more extreme than the observed value. A low p-value suggests that the observed outcome is unlikely to have happened by accident alone and provides strong evidence against the null hypothesis. To ascertain if the observed result is statistically significant, researchers usually assign a predetermined significance level (e.g., α = 0.05). The alternative hypothesis is accepted in place of the null hypothesis, suggesting a significant impact or difference, if the p-value is less than the significance level.
Permutation tests in Machine Learning
Permutation tests become quite useful in these situations, offering a reliable and adaptable substitute for hypothesis testing. The basic idea behind permutation testing is to generate a null distribution by randomly permuting the observed data. This allows for inference to be made without making strict assumptions about the distribution of the data. This article aims to demystify permutation tests in machine learning.
Table of Content
- What are Permutation Tests?
- Permutation Tests vs Traditional Parametric Tests
- Estimating the p-value in Permutation Tests
- P-values and its Interpretation in Permutation Tests
- Permutation Test in Python
- Benefits and Limitations of Permutation Test
- Applications of Permutation Tests in Machine Learning