There are various methods to identify outliers in epidemiological data:
1. Visual Inspection: - Boxplots: These graphical representations can help easily spot outliers as points outside the whiskers. - Scatter plots: Useful for bivariate data, revealing outliers in the context of two variables.
2. Statistical Methods: - Z-scores: Calculate the number of standard deviations a data point is from the mean. A common threshold is a Z-score greater than 3 or less than -3. - Interquartile Range (IQR): Outliers are often defined as data points that lie beyond 1.5 times the IQR above the third quartile or below the first quartile. - Grubbs' Test: A specific hypothesis test used to detect outliers in a univariate dataset.
3. Machine Learning Techniques: - Isolation Forest: This algorithm works by isolating observations in a random forest structure, identifying outliers as those that are isolated quickly. - Local Outlier Factor (LOF): This method identifies outliers by measuring the local density deviation of a given data point with respect to its neighbors.
