Final answer:
Outliers in data should not be excluded without analysis since they could be errors or may contain important information about the data set. Factors influencing whether to include or exclude an outlier comprise measurement irregularities and the specific circumstances affecting the outcome. Thorough examination is key to dealing with anomalous data.
Step-by-step explanation:
When dealing with anomalous data, also known as outliers, there are several methods that can be applied. However, not all methods are appropriate in every situation. An outlier might be an error or an abnormality, but it could also hold crucial information about the data set. To determine whether to exclude an outlier, one needs to investigate factors such as errors in data collection, irregularities in the object being measured, and other condition-specific factors that may affect the results. It's important not to dismiss outliers without proper scrutiny, as doing so may lead to oversights and incorrect conclusions. Conversely, automatically including all outliers without analysis may introduce bias or distort the representation of the data.
In circumstances where an outlier is a result of incorrect data due, for instance, to a measurement error or data entry mistake, it is generally appropriate to exclude it from the analysis. However, if the outlier reflects a true characteristic of the population or phenomenon being studied, it should be included and may require further investigation to understand its significance. Data analysts and scientists, including social scientists, must be vigilant not to label events as anomalies due to bias, while also recognizing that extraordinary circumstances can indeed occur. Thus, careful consideration is crucial when deciding how to handle anomalous data.