Data Quality Concepts

Outlier detection is the process of identifying data points that are unusually far from the rest of the dataset, using techniques like the IQR rule, z-scores, or visual inspection of box plots and scatter plots. These anomalous values may indicate measurement errors, data entry mistakes, or genuinely extreme observations.

"Outliers are data points that don't fit the pattern. A 7-foot student in a class of average heights, or a \$10 million house in a neighborhood of \$300k homes. They may be errors or genuinely unusual."

Why it matters: Outlier Detection helps students read data as a whole pattern instead of a pile of disconnected values. That habit matters because many statistical decisions depend on where a value sits in context, how symmetric the pattern is, and whether a simple summary would hide important structure.

Sampling bias occurs when a sample is collected in a way that systematically makes some members of the population more likely to be included than others, producing results that do not accurately represent the full population and leading to misleading conclusions.

"Asking only your friends about favorite music doesn't tell you what the whole school thinks - your friends probably have similar tastes! That's bias. A good sample is like a well-shuffled deck: everyone has an equal chance of being picked."

Why it matters: Sampling Bias matters because weak data collection can make a polished calculation meaningless. Students need to ask who was included, who was missed, and what population the conclusion can honestly describe.