What are the different types of data anomalies?
An example of an AI chatbot conversation about types of data anomalies.
What are the different types of data anomalies?
Data anomalies are irregularities in data that can affect analysis. The main types include outliers, noise, and missing values.
Can you explain what outliers are?
Outliers are data points that differ significantly from other observations. They can skew results and lead to misleading conclusions.
What causes data anomalies?
Data anomalies can be caused by errors in data collection, processing issues, or genuine variations in the data.
How can we detect these anomalies?
Anomalies can be detected using statistical methods, visualization techniques, or machine learning algorithms.
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