两组小鼠多器官比较：Two-way ANOVA与多重t检验选择问题

Hey Diego, let's break down which statistical test makes more sense for your experiment comparing experimental vs. control mice across 4 different organs.

Key Differences & Use Cases #

• Two-way ANOVA
  This is the right tool for your study design because it’s built to analyze the effects of two categorical variables (your group: experimental/control, and organ: 4 types) plus their interaction. It answers three critical questions:

  1. Is there an overall difference between the experimental and control groups across all organs?
  2. Do measurements vary significantly across the 4 organs, regardless of group?
  3. Most importantly—does the effect of the experimental treatment depend on the organ? (For example: maybe the treatment shifts liver values drastically but has no impact on kidney values.)
     It also avoids the biggest pitfall of multiple t-tests: inflated false positive rates.

• Multiple t-tests
  Running 4 separate t-tests (one per organ) might seem straightforward, but it comes with a major risk: Type I error inflation. With a standard α=0.05, each t-test has a 5% chance of a false positive. Run 4 tests, and the probability of at least one incorrect "significant" result jumps to ~18%. Even if you adjust for multiple comparisons (like Bonferroni correction), you still lose statistical power compared to using ANOVA first.

My Recommendation #

Stick with Two-way ANOVA for your analysis:

1. It gives you a complete picture of your data, including interaction effects that might be the most interesting finding in your study.
2. It controls for false positives far better than unadjusted multiple t-tests.
3. If the ANOVA returns a significant main effect or interaction, you can use Prism’s built-in post-hoc tests (like Tukey’s or Dunnett’s) to zoom in on exactly which organs show significant group differences—without sacrificing statistical rigor.

If you only cared about isolated organ-by-organ comparisons (and had zero interest in overall or interaction effects), adjusted multiple t-tests could work—but that’s rare in most biological studies, where understanding how treatments act across tissues is key.

内容的提问来源于stack exchange，提问作者Diego