ads2-common-traps-checklist

name	ads2-common-traps-checklist
description	Use this skill as a final ADS/ADS2 exam answer review checklist. It catches common mistakes in data cleaning, test choice, paired designs, ANOVA interactions, chi-square expected counts, pwr sample-size formulas, regression wording, clustering interpretation, and report language. Trigger on review my ADS answer, check mistakes, before submission, common traps, is this correct, or final checklist.

ADS2 Common Traps Checklist

Run this before final submission or when reviewing an answer.

Language

• Use "fail to reject H0", not "accept H0".
• Use "evidence suggests" or "is associated with", not "prove".
• Do not report only a p-value; write a contextual conclusion.
• Do not call a non-significant result "no effect"; mention insufficient evidence and power if relevant.

Data Handling

• Avoid personal absolute file paths in final RMarkdown.
• Do not remove missing values or duplicates silently.
• Do not treat repeated measures as independent observations.
• Convert categorical numeric codes to factors when needed.
• Merge paired files by ID, not row order unless order is verified.

Test Choice

• Paired before/after data needs paired tests.
• Independent groups need independent tests.
• For paired t-tests, check normality of differences.
• Ordinal data should not be treated as interval numeric without justification.
• More than two groups: use ANOVA/Kruskal-Wallis first, not many t-tests.
• Two-factor designs need factorial ANOVA and interaction consideration.

Chi-Square

• Check expected counts, not observed counts.
• Goodness-of-fit uses expected probabilities explicitly.
• Independence/homogeneity uses a count table or matrix.
• Use Fisher exact test if expected counts are too small.
• Do not run chisq.test() on a CrossTable display object; use the count matrix.

Power

• pwr.t.test(n = ...) uses per-group n for two-sample tests.
• pwr.anova.test(n = ...) uses per-group n.
• Cohen's f for ANOVA is sqrt(eta_sq / (1 - eta_sq)).
• A sample-size recommendation needs an effect size and target power.

Regression

• Correlation is not causation.
• Slope interpretation needs units.
• Intercept interpretation may be meaningless if x = 0 is outside the data range.
• Check residual plots before trusting linear model inference.
• Correlated predictors can make coefficient p-values unstable.

Clustering

• Set a seed for k-means.
• Scale numeric features if ranges differ.
• Cluster labels are arbitrary.
• Compare clusters to known labels with a table, not by color alone.