Topics covered include types of data, descriptive statistics (mean, median, mode, percentiles), the normal distribution, confidence intervals and the standard error of the mean, hypothesis testing and the choice of statistical tests, type 1 and 2 errors, contingency tables, correlation and regression, and meta-analysis.
As its name suggests, the Lit Lab tackles literary problems by scientific means: hypothesis-testing, computational modeling, quantitative analysis.
The package Persistence Landscape Toolbox [150] (see [70] for the accompanying tutorial) implements the computation of persistence landscapes, as well as many statistical tools that one can apply to persistence landscapes, such as mean, ANOVA, hypothesis tests, and many more.
The significance of the categories was evaluated by means of hypothesis testing using 50,000 permutations of the group labels.
The final permuted p-values were obtained by means of hypothesis testing using 50,000 permutations of the group labels.
The statistical significance of changes, from the before to after period, was determined by means of hypothesis tests, with a considered significance level of 95%.
Unlike existing methods, our approach addresses the problem by means of a hypothesis test that is based on the distribution of the recorded ion counts, and thereby provides a statistically rigorous measure of the uncertainty involved in the classification problem.
To test the mean of this statistics, a hypothesis test is derived based on the asymptotic local approach, which yields a χ2-test.
The best way to determine which explanation is most likely is to calculate a confidence interval for the mean and to perform a hypothesis test.
The validation process shows that all the models successfully pass the hypothesis test, which means that there were no significant differences between the model and the real data.
