This chapter explores the concept of outliers, data points that deviate significantly from the rest of a dataset. Outliers can arise from various causes, such as data entry errors, measurement inaccuracies, or genuine extreme events, and can heavily influence statistical analyses, especially measures such as the mean and standard deviation. Common methods for identifying outliers are discussed, including the very common Interquartile Range (IQR) method, which defines outliers as values beyond 1.5 times the IQR from the quartiles, and the z-score method, which identifies outliers based on their deviation from the mean in terms of standard deviations. Domain-specific criteria are also covered, highlighting how certain fields define outliers according to context. The chapter emphasises the importance of carefully treating and reporting outliers to ensure accurate data interpretation while also addressing the impact outliers can have on statistical results.