Extreme Observation or Data Error? Influence Diagnostics and Robust Analysis
Do not delete an observation because it changes significance; investigate errors, influence, leverage and robust sensitivity analyses.
Educational articles on statistical methods, SPSS usage, and academic research.
Do not delete an observation because it changes significance; investigate errors, influence, leverage and robust sensitivity analyses.
Assess missingness mechanisms before deleting observations and choose complete-case analysis, multiple imputation and sensitivity analyses appropriately.
Understand why correlated predictors destabilise regression coefficients, how to interpret VIF, and which remedies preserve the scientific question.
Learn how to analyse censored time-to-event data using Kaplan–Meier curves, log-rank tests and Cox regression while checking key assumptions.
Testing many hypotheses increases false-positive risk. This guide compares family-wise error and false discovery rate methods with practical examples.
Learn how to interpret odds ratios, 95% confidence intervals, p-values, calibration, and AUC in logistic regression with clinical examples.