Module 7: The Master Decision Framework: Choosing the Right Statistical Test
Tested Stacks:
GATK 4.6.1
Samtools 1.21
Nextflow 24.10
Seurat v5.1
Python 3.12
Prerequisites: Basic Linux CLI & Sequencing Formats
Module 07 • Test Selection Guide
Track 19 • Applied Environmental Statistics
Hands-On R & RStudio Included
Module 7: The Master Decision Framework: Choosing the Right Statistical Test
A structured decision tree for scientific research questions: continuous vs categorical responses, 2 groups vs 3+ groups, independent vs paired designs, parametric vs non-parametric alternatives, and contingency tests.
7. The Master Decision Framework: Choosing the Right Statistical Test
The most common anxiety among scientific researchers is deciding which statistical test applies to their data. By systematically answering four diagnostic questions, you can instantly identify the exact parametric or non-parametric test to run in R.
The 4-Step Test Selection Protocol:
- What is the nature of your Dependent (Response) Variable? Continuous numerical (e.g. pH, metal conc.), discrete count (species count), or categorical (presence/absence).
- What is the nature of your Independent (Predictor) Variable? Categorical treatment groups (Control vs Treatment), or continuous numerical predictor (Temperature).
- How many groups are you comparing? 2 groups vs 3 or more groups.
- Are the samples Independent or Paired? Independent (different plots/ponds) vs Paired/Repeated measures (before vs after spill at the same location).
- Are the Parametric Assumptions satisfied? Residual normality ($p > 0.05$ on Shapiro-Wilk) and equal variances (Levene's test).
The Universal Statistical Decision Matrix
| Research Objective | Experimental Design | Parametric Test (Normal Data) | Non-Parametric Counterpart (Skewed / Ranks) |
|---|---|---|---|
| Compare 2 Groups | Independent samples (e.g. Upstream vs Downstream) | Two-Sample Student's / Welch's $t$-test (t.test) | Mann-Whitney $U$ / Wilcoxon Rank-Sum (wilcox.test) |
| Compare 2 Groups | Paired samples (e.g. Same site Before vs After Rain) | Paired $t$-test (t.test(..., paired = TRUE)) | Wilcoxon Signed-Rank Test (wilcox.test(..., paired = TRUE)) |
| Compare ≥ 3 Groups | Independent (e.g. Forest vs Agri vs Urban vs Wetland) | One-Way ANOVA (aov) + Tukey HSD | Kruskal-Wallis Test (kruskal.test) + Dunn Test |
| Test Association | Two continuous variables (e.g. Water Temp vs DO) | Pearson Correlation ($r$) (cor.test) | Spearman Rank Correlation ($
ho$) (method = "spearman") |
| Model Relationship | Predict continuous $Y$ from continuous/categorical $X$ | Linear Regression (OLS) (lm) | Robust / Quantile Regression or GLM |
| Categorical Frequencies | Contingency table (e.g. Species presence across soil types) | Chi-Square Test ($\chi^2$) (chisq.test) | Fisher's Exact Test (for small $n < 5$) (fisher.test) |
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Common Pitfalls & Troubleshooting Checklist
- Java Heap Exhaustion (GATK / Picard): If processes terminate with
java.lang.OutOfMemoryError, set your environment variable explicitly:export _JAVA_OPTIONS="-Xmx16g -Djava.io.tmpdir=/tmp". - Index Timestamp Mismatch: If tools complain that a BAM index is missing or out of date, regenerate with
samtools index -@ 4 sample.bam. - Conda Channel Conflicts: Avoid ABI incompatibilities between bioconda and conda-forge by setting strict priority:
conda config --set channel_priority strict. - Chromosome Naming Contamination: Always verify whether reference FASTA headers use
chr1or1; divergent naming causes silent alignment drops.
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