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Stop using linear regression for binary data! If your pre-clinical research involves "Yes/No" outcomes—like mouse survival, tumor presence, or assay viability—you need Binary Logistic Regression. This guide bridges the gap between complex math and bench science, explaining exactly how to run, validate, and interpret logistic models. Learn how to handle Odds Ratios (OR), avoid "Perfect Separation" errors in small cohorts, and satisfy the statistical standards of top-tier biome

Choosing between Welch's t-test, data transformation, and non-parametric tests like Mann-Whitney U is a critical decision that affects your p-values and publication chances. While standard t-tests often fail due to unequal variances, blindly switching to non-parametric tests can kill your statistical power. This guide simplifies the decision process, explaining why Welch's t-test should be your default and when to truly embrace ranking methods.