Grubbs' test, Dixon's test, and Chauvenet's criterion are all statistical methods that can be used to detect outliers in oceanographic data.

Data quality control in oceanography is important for ensuring that the data is accurate and reliable, identifying and removing errors from the data, and making the data more useful for scientific research.

Instrumental error, human error, and computational error are all common types of errors that can occur in oceanographic data. Environmental error is not a common type of error.

Data cleaning, data editing, and data validation are all processes that can be used to remove errors from oceanographic data.

Scatter plots, line graphs, and contour plots are all common methods for visualizing oceanographic data. Bar charts are not a common method for visualizing oceanographic data.

When choosing a data quality control method, it is important to consider the type of data, the purpose of the data, and the resources available.

Data quality assessment reports, data validation reports, and data cleaning reports are all common types of data quality control reports. Data analysis reports are not a common type of data quality control report.

The final step in the data quality control process is data archiving. This involves storing the data in a safe and secure location for future use.

Ocean Data View, Python, and R are all common types of data quality control software. MATLAB is not a common type of data quality control software.

When performing data quality control, it is important to be thorough, consistent, and objective.

ISO 9001, ISO 17025, and All of the above are common types of data quality control standards. ISO 14001 is not a common type of data quality control standard.

To prevent errors from occurring in oceanographic data, it is important to use high-quality instruments, train personnel properly, and follow standard operating procedures.

Internal audits, external audits, and All of the above are common types of data quality control audits. Surveillance audits are not a common type of data quality control audit.

When choosing a data quality control auditor, it is important to consider their qualifications, experience, and independence.

On-the-job training, classroom training, and online training are all common types of data quality control training.