Definition:
- Standard deviation is a measurement of the dispersion of a set of data from its mean. It is the square root of the variance of the dataset.
- Mean deviation is a measure of dispersion that is the average absolute deviation of the data from the arithmetic mean of the data.

Calculation:
- Standard deviation is calculated by finding the square root of the sum of the squared differences between each data point and the mean, divided by the total number of data points.
- Mean deviation is calculated by finding the sum of the absolute differences between each data point and the mean, divided by the total number of data points.

Interpretation:
- Standard deviation measures the spread of the data and is affected by outliers. It is commonly used in statistical analysis to determine the range within which the majority of the data falls.
- Mean deviation gives an idea of how far the data points are from the mean on average. It is less affected by outliers, but it may not provide a complete picture of the data spread.

Use:
- Standard deviation is commonly used in finance, science, and engineering for analysis and decision making.
- Mean deviation is used less frequently than standard deviation, but it can be useful in certain situations, such as when dealing with data that has outliers.

Relationship:
- Standard deviation is generally larger than mean deviation, as it takes into account the squared differences between each data point and the mean.
- Mean deviation can be calculated using the standard deviation of the data, but it is not as commonly used as a measure of dispersion.

Conclusion:
- Standard deviation and mean deviation are both measures of dispersion, but standard deviation is more commonly used due to its ability to provide a more complete picture of the data spread. Mean deviation can be useful in certain situations, but it is not as widely used as standard deviation.