Identifying and Handling Outliers in Data

Miacademy & MiaPrep Learning Channel

This comprehensive statistics lesson explores the concept of outliers—data points that differ significantly from other observations in a dataset. The video provides a clear definition of outliers and demonstrates multiple methods for identifying them, ranging from visual inspection of scatter plots and box plots to precise mathematical calculation using the Interquartile Range (IQR) method. It walks students through step-by-step examples of calculating lower and upper bounds to mathematically pinpoint specific outlier values. Beyond identification, the video delves into the statistical impact of outliers. It compares how different measures of central tendency (mean, median, mode) and measures of dispersion (range, standard deviation) are affected when outliers are present versus when they are removed. This section emphasizes why the median is often a more robust measure than the mean in skewed datasets. Finally, the video discusses strategies for handling outliers in data analysis, presenting the pros and cons of removing them versus replacing them with other values like the mean or median. This critical thinking component encourages students to consider the context of the data—whether an outlier represents an error or a significant extreme case—before deciding how to treat it, making it an excellent resource for high school statistics and data science units.