The ability to separate good, mediocre, and poor quality data is a crucial data literacy skill. Data-driven conclusions are only as strong, robust, and well-supported as the data behind them. This is also often referred to with the phrase “garbage in, garbage out.”
Bias in data collection leads to poorer quality data. Recognizing bias in data is a crucial data literacy skill. Some key questions about bias include “Who made the data?”, “Who participated in the data?” and “Who is left out of the data?”
Statistics helps to measure whether an event happens by chance or by a systemic factor or factors. For example, it’s statistically more likely to see traffic during peak rush hour than outside of peak rush hour times.
Statistics can reveal systemic patterns in a data set rather than relying on individual experiences. This is important in legal cases including those addressing discrimination or class-action lawsuits.