Statistical Inference for Omics — t-tests, ANOVA & GLM — Hands-on

Build a solid, practice oriented understanding of statistical inference for omics and biomedical data. This module walks you from basic sampling distributions through t tests, ANOVA and generalized linear models, with a focus on assumptions, effect sizes, confidence intervals and reproducible reporting in R and Python.

Statistical Inference for Omics — t-tests, ANOVA, GLM

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Session Index

Session 1 — Foundations of Statistical Inference Session 2 — Group Comparisons: t-tests & ANOVA Session 3 — GLM for Omics and Count Data Session 4 — Omics Inference Clinic & Reporting

Session 1

Fee: Rs 8800

Foundations of Statistical Inference

Omics data types and distributions

continuous / counts / proportions mean variance relationship log and variance stabilizing transforms

Sampling distributions and central limit theorem

standard error and uncertainty bootstrap intuition finite sample caveats

Confidence intervals and p values

interval estimation vs testing one sided vs two sided practical vs statistical significance

Session 2

Fee: Rs 11800

Group Comparisons: t-tests & ANOVA

Two group and paired comparisons

independent and paired t tests Welch correction non parametric alternatives

One way and multi factor ANOVA

F statistic and variance decomposition interaction terms balanced vs unbalanced designs

Post hoc tests and assumptions

pairwise comparisons normality and variance checks robust and rank based options

Session 3

Fee: Rs 14800

GLM for Omics and Count Data

Linear models for omics signals

design matrices and contrasts covariate adjustment batch effects in models

Generalized linear models

logistic regression for binary traits Poisson and negative binomial for counts link functions and interpretation

Diagnostics and goodness of fit

residual plots overdispersion checks influence and leverage

Session 4

Fee: Rs 18800

Omics Inference Clinic & Reporting

Case studies with real omics datasets

gene expression and clinical cohorts

Reproducible analysis in R and Python

R stats and broom Python statsmodels and pingouin scripted workflows and notebooks

Deliverables: analysis report and scripts

PDF or HTML summary annotated R and Python code assumption and decision log

PDF

Statistical Inference for Omics — t-tests, ANOVA & GLM Training | Biostatistics & Omics Analytics

Statistical Inference for Omics — t-tests, ANOVA & GLM — Hands-on