Mann Whitney U test
Used to determine if there are significant differences between two independent groups, especially when data violates normality
Pearson’s R
Used to measure the strength and direction of a linear relationship between two continuous variables.
Kruskal Wallis test
Used to assess if there are significant differences among multiple groups when parametric assumptions are not met, making it suitable for non-normally distributed data.
N-way ANOVA + post-hoc test
Used to analyze the effects of multiple independent variables on a dependent variable. Post-hoc tests further identify specific group differences after ANOVA analysis.
Fisher’s exact test
Used when dealing with small sample sizes or rare events. It assesses the association between two categorical variables without assuming normality, making it suitable for sparse data.
Chi Squared test
Used to determine if there is a significant association between categorical variables. It compares observed and expected frequencies to assess independence or relationships.
T-test (Welch)
A variation of the Student’s t-test that handles unequal variances and sample sizes between two groups. It’s robust and useful for analyzing data with unequal characteristics.
T-test (Student)
Used to compare the means of two groups. It determines if the difference between their sample averages is significant or due to chance.
Z-Spearman’s Rho
Measures the strength and direction of the relationship between two ordinal variables. It assesses non-linear associations, making it valuable for ranked data.
Logistic regression
Used to model binary outcomes, such as yes/no or true/false. It assesses the probability of an event occurring based on one or more predictor variables.