Author

Eike Paulat

Read time

10 mins

Published

Jun 17, 2025

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Understanding data sampling in GA4: Causes, impact, and workarounds

We all know how frustrating it is to throw a perfect prompt into ChatGPT only to get hallucinations in response. No matter how well you adjust the instructions, each fixed mistake will inevitably be followed by two new ones. So, you either choose to do everything manually or accept the best of the worst generated versions.

The same problem may happen with data sampling in Google Analytics (GA). But the tricky part of GA sampling is that you can completely miss the bias when you’re unaware of how the data sampling procedure works and what inaccuracies it can introduce.

This guide will shed light on what happens behind the data sampling in Google Analytics. We’ll clarify potential data biases and what you can do to minimize their possible negative impacts.

What is data sampling?

A simple data sampling definition is that it’s the procedure of choosing and analyzing a subset of data to make conclusions about a larger set. In other words, data samples are mini versions of big data.

When you choose a relevant data sample, you don’t need to analyze each data unit in the large data set separately. Your selection is already illustrative enough because it contains everything you need to understand the full picture.

A sample of data is an analytical tool that’s frequently used in social sciences. The cost and resources required to survey a whole community, city, country, or global population is too high for researchers, so using a data sample is more efficient.

Sample data examples frequently appear in analytical surveys and reports:

• The U.S. Bureau of Labor Statistics reported American time use insights based on interviewing approximately 7,700 individuals.
• Deloitte has asked over 1,100 professionals across 28 countries to detect five key global tax policy trends in 2025.
• The Pew Research Center surveyed 28,333 people in 24 countries to learn about global opinions of the United States in 2025. 

Most statistics you read are highly likely to be based on a data sample. But while useful, data sampling can come at a price.

Why is sampling important?

Sampling data may not be perfect, but it provides a decent balance between speed and accuracy.

Sampling methods are important because, in large data sets, patterns tend to repeat and reinforce existing conclusions. Once those patterns are clear, there is no need to invest time and money into analyzing each unit in the data set. It’s more effective to apply data sampling to focus on the most representative sample for analysis.

GA4 sampling helps to get timely, customized reports for high data volumes. It saves computing power and gets you relevant reports without needing to analyze each data point in a massive data set separately.

Data sampling techniques

Given the cost of choosing the wrong sampled data, researchers have developed several sampling strategies to adjust the composition of a sample. The distinction between methods is the randomization principle, or whether they involve randomization. This divides types of sampling methods into two large groups: probability and nonprobability sampling.

Probability data sampling

Probability sampling requires that all the units and unit compositions have equal opportunities to be included in the data sample. The sampling procedure becomes similar to a lottery: each number and combination of numbers has an equal chance of being chosen.

All successful probability sampling types address these three concerns:

1. How to mark the data units so they get an equal chance to be included in a data sample
2. Which sampling techniques and rules should be applied to guarantee equal chances for all data units
3. Where the most inclusive starting point is for data sampling

Simple random data sampling

This is the ideal randomization situation, in which each unit has an equal chance or probability of being selected. 

This situation is ideal because in real life it is difficult to apply. It requires a complete list of every individual in the data set. For example, you’d need the names of every voter or the demographic details of all your website visitors. That level of detail is rarely available in real-world scenarios.

Systematic data sampling

This is the most realistic scenario in randomized data sampling. Instead of selecting units entirely at random, the researcher chooses a regular interval and applies it to a table of random numbers. For example, they might choose every fifth row in a data set.

Even though this sampling method is still based on a probability principle, it limits the data units once the interval is applied. Collecting a complete data set is also necessary here so you can apply the interval consistently.

Cluster data sampling

In cluster sampling, the researcher divides random data into groups, or clusters, based on a selected characteristic, like city of residence or a type of acquisition channel. They then perform randomized data sampling under carefully designed quotas to get that mini version.

Stratified data sampling

Under this sampling type, the researcher divides data into groups based on shared characteristics and performs sampling on them. 

This sampling method helps ensure that each subgroup is properly represented in the final sample. It is commonly used for small data subsets and lets researchers analyze both the individual strata and the combined data set with greater accuracy.

Nonprobability data sampling

Under nonprobability data sampling, the sample selection is usually curated based on a certain criterion. We’ll review these sampling techniques briefly, since they are not used in Google Analytics sampling.

Convenience data sampling

Under this method, the sample is chosen based on ease of access rather than randomness or representativeness. Researchers use data that is readily available, whether due to time constraints, location, or limited resources.

For example, a researcher might survey people walking by on campus or use the first 100 website sessions of the day, simply because that data is easiest to reach.

Purposive data sampling

Also referred to as expert sampling, this method determines the data sample composition based on a research purpose. It often means that a researcher looks for “ideal” data units that contain the necessary set of characteristics for their research objective.

Quota data sampling

Similar to purposive data sampling, the researcher composes the data sample with their research objective. They divide the data into specific categories and set a target number of samples for each. Then, they select individuals nonrandomly until the quotas are filled.

Data sampling: Pros and cons

Can you rely on sampled data?

Data sampling is a convenient and reliable analytical tool if its sampling method prioritizes maximum accuracy.

While working with data sampling, the researchers pay close attention to saving the representativeness of data in the mini versions. They often acknowledge the price of human error while choosing a sampling method as well as possible biases in a large data set. 

The most reliable data samples accurately reflect the composition of a larger data set. For example, the New York Times has counted for more polls that “meet certain criteria for reliability.” That means choosing likely voters (instead of all adults), a larger data sample, the most recent surveys, and researchers with an unbiased track record.

To address the representativeness issue, researchers should carefully design their surveys and choose appropriate types of sampling.  Otherwise, their sample won’t accurately represent the most important characteristics of a larger population. 

In marketing research, data sampling works much the same way, especially when generating web traffic reports. The whole procedure resembles the research from social sciences, but with GA4 taking on the role of the researcher.

How data sampling in GA4 works

Many believe that GA4 doesn’t sample data like Universal Analytics. In reality, each time you deal with explorations, large date ranges, and complex segments, Google Analytics starts applying data sampling. It notifies you that it has happened without asking you in advance.

What is data sampling in GA4?

GA4 sampling is when GA4 uses a subset of data to generate your reports more quickly and without using excessive computing power. Whether Google Analytics sampling occurs depends on if you’ve reached the quota limit for your data set, also called the sampling threshold. 

In GA4, data sampling is based on probability sampling once the data set size reaches a certain limit, meaning that it doesn’t apply any curation over the sampling method. 

This makes Google sampling closer to the ideal simple random probability approach from earlier. However, there is no available information on whether GA4 stratifies data in any way before applying randomization or not.

Causes for GA4 sampling

GA4 sampling primarily occurs in three situations:

1. Complex reports: You ask GA4 to generate detailed reports by analyzing multiple segments, applying multiple filters, and adding secondary dimensions or extra metrics.
2. Large data volume: The data set includes a number of sessions/events that exceed the quota limit.
3. Complex data composition: A high level of dimension cardinality, or the great number of unique values for each data dimension, makes the data set reach its limits sooner.

Data sampling in GA4 vs Universal Analytics vs GA 360

Google sampling occurs automatically when you try to generate a report, exploration, or request from a number of events that exceeds a quota limit.

How to detect Google Analytics sampling

When GA4 uses data sampling, you’ll see the yellow warning icon with the percentage of data used to create your results.

GA4 can also show you the data quality indicator and the percentage of data used for the report.

You can spot data sampling, especially due to high cardinality, by looking for things like “other” rows in your reports, unexpectedly flat trends, or missing granular data.

What can go wrong with data sampling in GA4

Given that data sampling isn’t perfect, marketers should be ready to read any insights from reports and understand the possible trade-offs in accuracy and completeness. 

How data sampling affects marketing decisions

• Inaccurate conversion paths: If you work with multi-touch conversion paths, the loss of data nuance in GA4 sampling increases the risk of a misleading campaign focus.
• Skewed attribution modeling: Data sampling leaves out low-frequency touchpoints, so the GA4 report may assign incorrect values to certain channels.
• Misleading performance of ad campaigns or user journeys: Sampled data may distort performance metrics, including Return on Objective (ROO) calculations and optimization strategies.

In real life, researchers have a chance to choose between different sampling methods and decide on a sample size and a sampling technique to use if needed. But in GA4, you don’t have control over how the data is sampled. 

Although random sampling is often considered the best data sampling technique, at times, marketers want more control over the sampled data composition. Rare audience segments and dramatic traffic spikes can be missed in a sample subset. A more curated sampling procedure might’ve helped create a more balanced sample if it were possible.

Data sampling workarounds: Quick fixes

Although you cannot eliminate Google Analytics sampling for large and complex data, you can use one of these tactics to get more accurate results in your report.

Use shorter date ranges

The data size may exceed the limit because you have chosen a data range that’s too large. Narrow the range or generate separate reports for several data ranges to double-check the results from sampled data.

Simplify segments or filters

To avoid cardinality issues, try limiting the number of segments and filters. This way, GA4 won’t require that much computing power to analyze the data and may not launch data sampling.

Update to GA4 360

GA4 360 can analyze up to one billion events without using sampling techniques. You can upgrade to this version through the data quality icon.

Use BigQuery for raw data exports

If your data set exceeds the GA4 360 quota limit, you can integrate BigQuery to see unsampled event-level data. Connect it to server-side tracking tools or Google Cloud Platform (GCP) integrations for higher accuracy, privacy compliance, and real-time insights.

Why this matters in a privacy-first world

Privacy regulations and consent requirements can introduce gaps and inconsistencies in your data set. They create challenges for data sampling accuracy and reliability. 

When randomization is limited — such as with incomplete or diminished data sets — sampling becomes less reliable. This increases the risk of bias, affects compliance reporting, and may lead marketers to inaccurate conclusions. 

In marketing, inaccurate conclusions can lead to costly mistakes.

To eliminate this compounding uncertainty, you need access to reliable data. To get there you can either tweak your data inside GA4 or get BigQuery integration strengthened by server-side tracking for data reliability.

Next steps: Get accuracy and privacy compliance with server-side tracking

If you’re serious about eliminating sampling issues and gaining full control over your analytics data, combine server-side tagging with BigQuery in GA4 for unsampled, privacy-compliant tracking.

Here are some key benefits of server side tracking:

• Export raw, unsampled GA4 data directly into BigQuery.
• Ensure better data accuracy for attribution, KPIs, and compliance audits.
• Collect and process data in a way that’s privacy-first and consent-compliant