Applied Biostatistics: Using Appropriate Statistics for Your Biological Data

Conducted every alternate Sunday (10:00 AM – 5:00 PM IST · Live Online). Kindly register below and our team will get back to you with the upcoming cohort schedule and onboarding details.

Workshop Overview

Misapplied statistical tests are one of the top reasons life-science manuscripts are rejected during peer review. This practical workshop teaches you how to inspect your data distributions, apply appropriate data filtering and transformations, and confidently choose and execute the exact statistical model your experimental design requires.

1. Appropriate Data Filtering, Normalization & Transformation

• Inspecting raw biological datasets: outlier detection, missing-value handling, and filtering low-signal or noisy measurements.

• Testing statistical assumptions: normality diagnostics (Shapiro–Wilk test, Q–Q plots, skewness) and homogeneity of variance (Levene's and Bartlett's tests).

• Applying appropriate data transformations: log2 / log10 transformation (with pseudocounts), square-root, Box–Cox, arcsine-square-root for proportions, Z-score standardization, and centered log-ratio (CLR) for compositional data.

2. How to Decide: Paired t-Test, Unpaired t-Test, or ANOVA?

• A clear decision framework based on experimental design: number of groups, independence of observations, and outcome variable type.

• When to use an Unpaired (Student's / Welch's) t-test vs. a Paired t-test (before-and-after measurements on the same subject or matched biological samples).

• One-way and Two-way ANOVA (with interaction terms) for multi-group and factorial experiments, followed by appropriate post-hoc tests (Tukey HSD, Dunnett's test, Bonferroni, Šidák).

3. Non-Parametric Statistics: Wilcoxon Rank-Sum & Signed-Rank Tests

• Recognizing when data transformations cannot rescue skewed, ordinal, or small-sample biological distributions.

• Running and interpreting the Wilcoxon Rank-Sum test (Mann–Whitney U) for independent groups and the Wilcoxon Signed-Rank test for paired data.

• Multi-group non-parametric testing: Kruskal–Wallis test and Dunn's post-hoc comparison with multiple-testing correction.

4. Linear Mixed-Effects Models (LMM) for Complex & Longitudinal Designs

• Why standard t-tests and ANOVA fail when you have repeated measures, longitudinal time points, batch effects, multi-center cohorts, or animals nested within cages/litters (pseudoreplication).

• Formulating Linear Mixed Models (LMMs) using fixed effects and random intercepts/slopes (lme4 / lmerTest in R).

• Model diagnostics, likelihood-ratio tests, estimated marginal means (emmeans), and reporting effect sizes, 95% confidence intervals, and FDR-adjusted p-values (Benjamini–Hochberg).