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.

When to use ✅✅✅

Multiple Independent Variables: N-way ANOVA is suitable when you have two or more independent variables (factors) influencing a dependent variable.
Comparing Means of Multiple Groups: It helps compare means across multiple groups or categories of your factors.
Identifying Interactions: N-way ANOVA explores interactions between different factors, revealing how they jointly affect the dependent variable.
Complex Experimental Designs: Useful for analyzing data from experiments with multiple factors and different levels.
Post-hoc Analysis: When significant effects are found in the ANOVA, post-hoc tests help identify which specific group differences are significant.

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Statistical tests

There’s a diverse range of statistical tests, each tailored to specific situations. Make sure you use the right one! 😉

Z-test

Assesses whether the means of two populations are significantly different. It is based on the standard normal distribution and requires a known population standard deviation.

Linear regression

Used to examine the relationship between two or more variables. It helps identify and predict trends, making it valuable in various fields like economics and data analysis.

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.

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.

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.

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.

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.

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.

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.

Pearson’s R

Used to measure the strength and direction of a linear relationship between two continuous variables.

Mann Whitney U test

Used to determine if there are significant differences between two independent groups, especially when data violates normality