HYPOTHESIS TESTING IN R
Hypothesis testing is a statistical procedure used to make decisions or draw conclusions about the characteristics of a population based on information provided by a sample
NORMALITY TESTS
Normality tests are used to evaluate whether a data sample follows a normal distribution. These tests allow to verify if the data have a behavior similar to that of a Gaussian distribution, being useful to determine if the assumptions of certain parametric statistical analyses that require normality in the data are met
GOODNESS OF FIT TESTS
These tests are used to verify whether a proposed theoretical distribution adequately matches the observed data. They are useful to assess whether a specific distribution fits the data well, allowing to determine whether a theoretical model accurately represents the observed data distribution
Pearson's Chi-squared test with chisq.test()
chisq.test()
Kolmogorov-Smirnov test with ks.test()
ks.test()
MEDIAN TESTS
Median tests are used to test whether the medians of two or more groups are statistically different, thus identifying whether there are significant differences in medians between populations or treatments
Wilcoxon signed rank test
wilcox.test()
Wilcoxon rank sum test (Mann-Whitney U test)
wilcox.test()
Kruskal Wallis rank sum test (H test)
kruskal.test()
OTHER TYPES OF TESTS
There are other types of tests, such as tests for comparing means, for equality of variances or for equality of proportions
