Sampling

When a tag has collected a large amount of statistical data, Yandex Metrica can use only a subset of it. For example, it can process 1/10 of all sessions (and then multiply the results by 10 as needed).

This process of creating a data subset is called sampling. Sampling provides a balance between the speed of obtaining results and their accuracy.

Due to sampling, reports may exclude data from low-traffic URLs or very rare keywords.

If you want to always use a complete dataset without sampling for your analytics, subscribe to Yandex Metrica Pro.

You can control sampling using the accuracy request parameter governing the sample size for calculations.

This parameter can accept several values:

• low: Returns a fast result based on a limited data sample.
• medium: Returns the result based on a sample that combines speed and data accuracy.
• high: Returns the most precise value by using the largest data sample. This mode may require more time to process your data request.
• full: Returns all data.

This parameter can also take a numerical value from the (0,1] interval:

• 1: No sampling (corresponds to the full value).
• 0.1 or 0.01: The share of returned data (10%, 1%). Any value (for example, 0.42) will be rounded to the nearest power of 10.

By default, the accuracy parameter is set to medium.

In the returned results, the applied sampling is described using the following parameters:

• sampled: Whether data sampling was performed (true if it was performed, false if not).
• sample_share: The share of data used for calculating the result (value from 0 to 1).
• sample_size: Number of rows in the data sample.
• sample_space: Total number of rows in the source data (without sampling).