Statistical errors affect data quality and analysis validity. Type I error (α): rejecting true null hypothesis (false positive). Type II error (β): accepting false null hypothesis (false negative). Power of test: 1 - β, probability of correctly rejecting false null. Sampling error: difference between sample and population statistics. Non-sampling errors: measurement, response, processing errors. Key concepts: trade-off between Type I and II errors. Common traps: confusing error types, miscalculating error rates. Exam tips: identify error type from problem context. Time-saving: remember α affects critical value, β relates to power. Applications: quality control, medical testing, market research. Significance level (α): typically 0.05, determines rejection region. Confidence interval: (1 - α) × 100% indicates precision. Understanding errors crucial for hypothesis testing. Practice identifying error types in scenarios.