You are profiling data by using Azure Machine Learning studio. You need to detect columns with odd or missing values. Which statistic should you analyze?

'Profile' is a general term referring to the overall data profiling report, not a specific statistic used to pinpoint odd or missing values.

'Std deviation' (standard deviation) measures the spread or dispersion of data points around the mean, which is useful for understanding variability but does not directly identify individual odd or missing values.

The 'Error count' statistic in Azure Machine Learning's data profiling explicitly quantifies the number of invalid or missing values within a particular column. This directly helps identify data quality issues such as odd or missing entries that do not conform to expected patterns or types.

'Type' refers to the inferred data type of a column, and while type mismatches can cause errors, the 'Error count' is the metric that directly reports the number of problematic entries.