What's the best way to check for differences between groups? T-test, chi-square test, or ANOVA? A quick and simple guide

In biomedical research, testing for differences between groups is a common practice. For example, researchers compare mean quality of life scores before and after an intervention or compare T3 hormone concentrations between primigravida and multigravida pregnant people. How do researchers ensure that any differences observed between groups are not due to chance? Choosing the right statistical test is necessary to ensure that your results are reliable and your research is credible. Here are 5 important considerations when choosing a statistical test to check for differences between groups. 

Conclusion 

In order to reliably demonstrate differences between two or more groups, it's necessary to consider the characteristics of your data and choose a suitable statistical test. Always take into account what kind of variables you are analyzing, how they are distributed, how many groups you have to compare, whether they are related, and how many outcome variables you are focusing on.  

Would you like expert advice on running between-group and within-group comparisons? Consult a seasoned biostatistician, under Editage's Statistical Analysis & Review Services.