How is statistical significance measured and what determines if a result is statistically significant?
Statistical significance is a determination by an analyst that the results in the data are not explainable by chance alone. Statistical hypothesis testing is the method by which the analyst makes this determination. A p-value of 5% or lower is often considered to be statistically significant.
How do you know if results are statistically significant in psychology?
The smaller the p-value, the stronger the evidence that you should reject the null hypothesis.
- A p-value less than 0.05 (typically ≤ 0.05) is statistically significant.
- A p-value higher than 0.05 (> 0.05) is not statistically significant and indicates strong evidence for the null hypothesis.
Can you test for statistical significance of multiple classifiers?
Yes you can apply statistical hypothesis testing. You evaluate each classifier with cross-validation (like you did) but instead store the (accuracy/other metric) of that classifier during each fold.
Which is the best Test to compare classifiers?
P.N: There are other statistical tests used to compare classifiers, but those are the most recommended one. Independent data samples: used when having an unlimited dataset. You collect n samples for the train, and test dataset. Then calculate ten independent model scores for each method. Finally, apply the t-test to compare models.
How is the McNemar test used in statistics?
In statistics, McNemar’s test is a statistical test used on paired nominal data. It is applied to 2 × 2 contingency tables with a dichotomous trait, with matched pairs of subjects, to determine whether the row and column marginal frequencies are equal (that is, whether there is “marginal homogeneity”).
How to find statistical significance of multiple algorithms?
The folds should be shuffled by a random generator, but the same random folds should be used with each algorithm. Use a one sample t-test on the difference of performance. T-tests also assume i.i.d. samples. The independence of the samples is not given in CV. This problem can be avoided by using very different non-parametric tests.