As an immunology student, I’m sure you’re no stranger to cross-sectional studies. But when it comes to analyzing cell counts from different time points, things can get a bit tricky. You’ve got box plots with medians, but you want to quantify the differences between them. The question is, how do you do it?
First, let’s talk about what not to do. Hypothesis testing methods like ANOVA and chi-square are great for inferential statistics, but they’re not the right fit for cross-sectional studies. So, what’s the alternative?
One approach is to use prevalence ratios, which are commonly used in epidemiology. But are they applicable to your study? The answer is, it depends. If you’re looking at the proportion of cells in different treatment states, prevalence ratios might be a good choice. However, if you’re interested in comparing the actual cell counts, you might need to look elsewhere.
So, what are your options? One possibility is to use a non-parametric test like the Wilcoxon rank-sum test to compare the distributions of cell counts between different treatment groups. Another approach is to use a generalized linear model to model the relationship between the cell counts and the treatment states.
Ultimately, the key is to choose a method that aligns with your research question and the nature of your data. By going beyond simple box plots and medians, you can gain a deeper understanding of the relationships between your variables and make more informed conclusions about your study.