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Data anomalies: Difference between revisions
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(Created page with "== Introduction == Data anomalies refer to irregularities or deviations in datasets that can distort the results of data analysis. These anomalies can arise from various sources, including errors in data collection, data entry, or data processing. Understanding and addressing data anomalies is crucial for ensuring the accuracy and reliability of data-driven decisions. == Types of Data Anomalies == Data anomalies can be broadly classified into three categories: outliers,...") |
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Outliers are data points that significantly differ from other observations in a dataset. They can result from measurement errors, data entry mistakes, or genuine variability in the data. Outliers can skew statistical analyses and lead to incorrect conclusions. | Outliers are data points that significantly differ from other observations in a dataset. They can result from measurement errors, data entry mistakes, or genuine variability in the data. Outliers can skew statistical analyses and lead to incorrect conclusions. | ||
[[Image:Detail-96029.jpg|thumb|center|A scatter plot showing a cluster of data points with one point significantly distant from the rest.|class=only_on_mobile]] | |||
[[Image:Detail-96030.jpg|thumb|center|A scatter plot showing a cluster of data points with one point significantly distant from the rest.|class=only_on_desktop]] | |||
=== Missing Data === | === Missing Data === | ||