Esri ArcGIS Pro Associate Pro Certification Practice Exam

What is meant by the term 'data outliers' in data exploration?

1. Incorrectly entered data

2. Data points that differ significantly from others

3. Missing data points

4. Standard data values

The correct answer is: Data points that differ significantly from others

The term 'data outliers' refers to data points that differ significantly from other observations in a dataset. These outliers can be much larger or smaller than the majority of data points, and they can play a crucial role in data analysis because they may indicate variability in the measurement, experimental errors, or novel insights that warrant further investigation. Identifying outliers is important during data exploration as they can heavily influence statistical analyses, such as means and variances, and may lead to misleading interpretations if not properly addressed. For example, in a survey measuring household incomes, a few extremely high incomes would be considered outliers that could skew the average income calculation. The other concepts presented, such as incorrectly entered data, missing data points, and standard data values, do not align with the definition of outliers. Incorrectly entered data refers to mistakes made during data entry rather than naturally occurring values. Missing data points indicate the absence of information, which does not reflect the nature of outliers. Standard data values represent normal observations within the dataset, contrasting with the significance of outliers that stand out from those norms. Understanding the distinct characteristics of outliers is vital for effective data analysis and ensuring robust conclusions can be drawn from the data.