Final answer:
The type of edge case resulting from missing or incorrect values in data is known as errors, which includes outliers and noise. Outliers are significant deviations and need to be assessed to determine their impact on data analysis. Noise is random variability within the data, while chance and nonsampling errors are broader categories affecting the accuracy of data interpretation.
Step-by-step explanation:
A type of edge case that results from missing or incorrect values is known as errors. Errors can encompass a range of issues such as outliers and noise, which can affect the interpretation of data. Outliers are data points that are significantly different from others and can arise due to abnormalities or errors. They can have a large impact on statistical analyses because they may not be close to the best-fit line in a graph, such as a least-squares line. It is important to distinguish outliers to assess whether they are a result of error or if they represent a meaningful deviation within the data set. On the other hand, noise refers to random variability or meaningless fluctuations in the data.
When analyzing data, we must be cautious of potential errors, which include both sampling and nonsampling errors. Sampling errors occur due to the process of selecting a sample, like when a sample size is too small and does not represent the entire population. This is also referred to as chance error. Nonsampling errors are those not related to the process of selecting the sample but may be caused by external factors, such as a defective counting device.