Data Sampling: Uncovering Meaningful Information in Large Datasets
  • 3 Minutes to read
  • Dark

Data Sampling: Uncovering Meaningful Information in Large Datasets

  • Dark

Article summary

Data sampling is a statistical technique used in the field of data analysis and machine learning. It involves selecting a subset of data points from a larger dataset to represent and analyze. The goal of data sampling is to draw meaningful and reliable conclusions without the need to process the entire dataset.

Data sampling is particularly useful when working with large datasets where it might be impractical or resource-intensive to analyze the entire dataset. It helps in reducing computational costs, speeding up analysis, and providing insights into the overall population based on a representative sample. However, it's crucial to ensure that the selected sample is truly representative of the entire dataset to avoid biased conclusions.

There are various methods of data sampling, and the choice of method depends on the specific goals of the analysis and the characteristics of the dataset. Some common sampling methods include:

  • Random Sampling: Selecting data points randomly from the dataset, giving each data point an equal chance of being chosen.
  • Stratified Sampling: Dividing the dataset into different strata or groups based on certain characteristics and then randomly sampling from each stratum. This ensures representation from each subgroup.
  • Systematic Sampling: Selecting every nth item from the dataset after an initial random start. This method is useful when there is an inherent order or structure in the data.
  • Cluster Sampling: Dividing the dataset into clusters, randomly selecting some clusters, and then sampling all data points within the selected clusters.

Simplified Example
The goal is to estimate the number of trees in a 100-acre area with relatively uniformly distributed trees. To approximate the total, you could count trees in 1 acre and multiply by 100, or in half an acre and multiply by 200.

Google Data Sampling

Data sampling in Google Analytics 4 (as well as the discontinued Universal) and Google Search Console is essential for maintaining the balance between speed, accuracy, and cost-effectiveness in data analysis.

Data sampling allows these tools to provide actionable insights from large datasets without overwhelming users with information or incurring prohibitive computational costs.

However, as sampling may not always represent the entire data set accurately, this will be why there are discrepancies in reports.

Google Analytics 4

In Google Analytics 4, default reports under the Reports snapshot tab are unsampled. Sampling may occur in advanced analysis, like cohort, exploration, segment overlap, and funnel analysis.

For more information, see Google's official documentation.

Google Search Console

In Google Search Console, when grouping data by page or query, some information might be omitted for quicker computation.

To obtain precise data, we recommend grouping data by one of the following, or their combination:

  • Date
  • Device
  • Country

If you choose to group by page or query, the extracted totals will not align with the GSC UI.

For more information, see Google Search Console's official documentation.

Google Analytics (Deprecated)

Google Analytics employs data sampling to process vast data volumes efficiently.

Please note that Google Analytics Universal has been discontinued and stopped processing data.

Default reports in Google Analytics are free from sampling, while ad-hoc queries are subject to specific thresholds:

  • Analytics Standard: Up to 500k sessions for your chosen date range.
  • Analytics 360: Up to 100M sessions, with certain queries (events, custom variables, dimensions, and metrics) limited to 1M sessions. Historical data is available for up to 14 months.

Cardinality limits also apply:

  • Daily processed tables: 50k rows for Universal Analytics, 75k for Analytics 360.
  • Multi-day processed tables: 100k rows for Universal Analytics, 150k for Analytics 360.

Factors like complex analytics implementations, view filters, and query complexity can result in fewer sessions being sampled than the stated thresholds.

Was this article helpful?