Improving Data Quality: Dealing with Outliers in Scatter Plots

What is the best course of action when a data analyst notices a data point that is very different from the norm in a scatter plot?

a. remove b. investigate c. hide d. move

Answer:

When a data analyst notices a data point that is very different from the norm in a scatter plot, the best course of action is to investigate the outlier.

An outlier in a dataset is a data point that significantly deviates from the overall pattern of the other data points. It is important for data analysts to carefully handle outliers to ensure the quality and accuracy of their analysis.

Choosing to investigate the outlier is the correct approach because simply removing the outlier can lead to the loss of valuable information and potentially distort the analysis results. By investigating the outlier, the data analyst can gain insights into the underlying cause of the unusual data point.

During the investigation, the analyst may discover data errors, measurement issues, or rare occurrences that could have contributed to the presence of the outlier. This deeper dive into the outlier allows for a more accurate and comprehensive analysis of the dataset.

By understanding the reasons behind the outlier, the data analyst can make informed decisions on how to handle it within the analysis. This thorough investigation helps improve the quality of the data analysis and leads to more reliable insights.