About Firebase A/B tests

To help you maximize the relevance and usefulness of your test results, this page provides detailed information about how Firebase A/B Testing works.

Sample size

Firebase A/B Testing inference does not require the identification of a minimum sample size prior to starting an experiment. In general, you should pick the largest experiment exposure level that you feel comfortable with. Larger sample sizes increase the chances of finding a statistically significant result, especially when performance differences between variants are small. You may also find it useful to consult an online sample size calculator to find the recommended sample size based on the characteristics of your experiment.

Edit experiments

You can edit selected parameters of running experiments, including:

  • Experiment name
  • Description
  • Targeting conditions
  • Variant values

To edit an experiment:

  1. Open the results page for the experiment you want to modify.
  2. From the More menu, select Edit running experiment.
  3. Make your changes, then click Publish.

Note that changing the app's behavior during a running experiment may impact results.

Remote Config variant assignment logic

Users who match all experiment targeting conditions (including the percentage exposure condition) are assigned to experiment variants according to variant weights and a hash of the experiment ID and the user's Firebase installation ID.

Google Analytics Audiences are subject to latency and are not immediately available when a user initially meets the audience criteria:

  • When you create a new audience, it may take 24-48 hours to accumulate new users.
  • New users are typically enrolled into qualifying audiences 24-48 hours after they become eligible.

For time-sensitive targeting, consider the use of Google Analytics user properties or built-in targeting options such as country or region, language, and app version.

Once a user has entered an experiment, they are persistently assigned to their experiment variant and receive parameter values from the experiment as long as the experiment remains active, even if their user properties change and they no longer meet the experiment targeting criteria.

Activation events

Experiment activation events limit experiment measurement to app users who trigger the activation event. The experiment activation event does not have any impact on the experiment parameters that are fetched by the app; all users who meet the experiment targeting criteria will receive experiment parameters. Consequently, it is important to choose an activation event that occurs after the experiment parameters have been fetched and activated, but before the experiment parameters have been used to modify the app's behavior.

Variant weights

During experiment creation, it is possible to change the default variant weights to place a larger percentage of experiment users into a variant.

Interpret test results

Firebase A/B Testing uses frequentist inference to help you understand the likelihood that your experiment results could have occurred solely due to random chance. This likelihood is represented by a probability value, or p-value. The p-value is the probability that a difference in performance this large, or larger, between two variants could have occurred due to random chance if there is actually no effect, measured by a value between 0 and 1. A/B Testing uses a significance level of 0.05 so that:

  • A p-value less than 0.05 indicates that if the true difference were zero, there is a less than 5% chance that an observed difference this extreme could occur randomly. Because 0.05 is the threshold, any p-value less than 0.05 indicates a statistically significant difference between variants.
  • A p-value greater than 0.05 indicates that the difference between variants is not statistically significant.

Experiment data is refreshed once a day, and the last update time appears at the top of the experiment results page.

The experiment results graph displays the cumulative average values of the selected metric. For example, if you're tracking Ad revenue per user as a metric, it displays observed revenue per user and if you're tracking Crash-free users, it tracks the percentage of users who have not encountered a crash. This data is cumulative from the beginning of the experiment.

Results are split into Observed data and Inference data. Observed data is calculated directly from Google Analytics data, and inference data provides p-values and confidence intervals to help you evaluate the statistical significance of the observed data.

For each metric, the following statistics are displayed:

Observed data

  • Total value for the tracked metric (number of retained users, number of users who crashed, total revenue)
  • Metric-specific rate (retention rate, conversion rate, revenue per user)
  • Percent difference (lift) between the variant and baseline

Inference data

  • 95% CI (Difference in means) displays an interval that contains the "true" value of the tracked metric with 95% confidence. For example, if your experiment results in a 95% CI for estimated total revenue between $5 and $10, there is a 95% chance that the true difference in means is between $5 and $10. If the CI range includes 0, a statistically significant difference between the variant and baseline was not detected.

    Confidence interval values appear in the format that matches the tracked metric. For example, Time (in HH:MM:SS) for user retention, USD for ad revenue per user, and percentage for conversion rate.

  • P-value, which represents the probability of observing data as extreme as the results obtained in the experiment, given that there is no true difference between the variant and baseline. The lower the p-value, the higher the confidence that the observed performance remains true if we repeat the experiment. A value of 0.05 or lower indicates a significant difference and a low likelihood that results were due to chance. P-values are based on a one-tailed test, where the Variant value is greater than the Baseline value. Firebase uses an unequal variance t-test for continuous variables (numeric values, like revenue) and a z-test of proportions for conversion data (binary values, like user retention, crash-free users, users who trigger a Google Analytics event).

The experiment results provide important insights for each experiment variant, including:

  • How much higher or lower each experiment metric is compared to the baseline, as directly measured (that is, the actual observed data)
  • The likelihood that the observed difference between the variant and the baseline could have occurred due to random chance (p-value)
  • A range that is likely to contain the "true" performance difference between the variant and the baseline for each experiment metric---a way to understand the "best case" and "worst case" performance scenarios

Interpret results for experiments powered by Google Optimize

Firebase A/B Testing results for experiments started before October 23, 2023 were powered by Google Optimize. Google Optimize used Bayesian inference to generate insightful statistics from your experiment data.

Results are split into "observed data" and "modeled data." Observed data was calculated directly from analytics data, and modeled data was derived from the application of our Bayesian model to the observed data.

For each metric, the following statistics are displayed:

Observed Data

  • Total value (sum of metric for all users in the variant)
  • Average value (average value of metric for users in the variant)
  • % difference from baseline

Modeled Data

  • Probability to beat baseline: how likely that the metric is higher for this variant compared to the baseline
  • Percent difference from baseline: based on the median model estimates of the metric for the variant and the baseline
  • Metric ranges: the ranges where the value of the metric is most likely to be found, with 50% and 95% certainty

Overall, the experiment results give us three important insights for each variant in the experiment:

  1. How much higher or lower each experiment metric is compared to the baseline, as directly measured (i.e., the actual observed data)
  2. How likely it is that each experiment metric is higher than the baseline / best overall, based on Bayesian inference (probability to be better / best respectively)
  3. The plausible ranges for each experiment metric based on Bayesian inference--"best case" and "worst case" scenarios (credible intervals)

Leader determination

For experiments using Frequentist inference, Firebase declares that a variant is leading if there is a statistically significant performance difference between the variant and the baseline on the goal metric. If multiple variants meet this criteria, the variant with the lowest p-value is chosen.

For experiments that used Google Optimize, Firebase declared that a variant is a "clear leader" if it had greater than 95% chance of being better than the baseline variant on the primary metric. If multiple variants met the "clear leader" criteria, only the best performing variant overall was labeled as the "clear leader."

Since leader determination is based on the primary goal only, you should consider all relevant factors and review the results of secondary metrics before deciding whether or not to roll out a leading variant. You may want to consider the expected upside of making the change, the downside risk (such as the lower end of the confidence interval for improvement), and the impact to metrics other than the primary goal.

For example, if your primary metric is Crash-free users, and Variant A is a clear leader over the baseline, but Variant A user retention metrics trail baseline user retention, you may want to investigate further before rolling out Variant A more widely.

You can roll out any variant, not just a leading variant, based on your overall evaluation of performance across both primary and secondary metrics.

Experiment duration

Firebase recommends that an experiment continue to run until the following conditions are met:

  1. The experiment has accrued enough data to provide a useful result. Experiments and result data are updated once daily. You may want to consult an online sample size calculator to evaluate the recommended sample size of your experiment.
  2. The experiment has run long enough to ensure a representative sample of your users and measure longer-term performance. Two weeks is the recommended minimum runtime for a typical Remote Config experiment.

Experiment data is processed for a maximum of 90 days after experiment start. After 90 days, the experiment is automatically stopped. Experiment results are no longer updated in the Firebase console and the experiment stops sending experiment-specific parameter values. At this point, clients begin fetching parameter values based on the conditions set in the Remote Config template. Historical experiment data is retained until you delete the experiment.

BigQuery schema

In addition to viewing A/B Testing experiment data in the Firebase console, you can inspect and analyze experiment data in BigQuery. While A/B Testing does not have a separate BigQuery table, experiment and variant memberships are stored on every Google Analytics event within the Analytics event tables.

The user properties that contain experiment information are of the form userProperty.key like "firebase_exp_%" or userProperty.key = "firebase_exp_01" where 01 is the experiment ID, and userProperty.value.string_value contains the (zero-based) index of the experiment variant.

You can use these experiment user properties to extract experiment data. This gives you the power to slice your experiment results in many different ways and independently verify the results of A/B Testing.

To get started, complete the following as described in this guide:

  1. Enable BigQuery export for Google Analytics in the Firebase console
  2. Access A/B Testing data using BigQuery
  3. Explore example queries

Enable BigQuery export for Google Analytics in the Firebase console

If you're on the Spark plan, you can use the BigQuery sandbox to access BigQuery at no cost, subject to Sandbox limits. See Pricing and the BigQuery sandbox for more information.

First, make sure that you're exporting your Analytics data to BigQuery:

  1. Open the Integrations tab, which you can access using > Project settings in the Firebase console.
  2. If you're already using BigQuery with other Firebase services, click Manage. Otherwise, click Link.
  3. Review About Linking Firebase to BigQuery, then click Next.
  4. In the Configure integration section, enable the Google Analytics toggle.

  5. Select a region and choose export settings.

  6. Click Link to BigQuery.

Depending on how you chose to export data, it may take up to a day for the tables to become available. For more information about exporting project data to BigQuery, see Export project data to BigQuery.

Access A/B Testing data in BigQuery

Before querying for data for a specific experiment, you'll want to obtain some or all of the following to use in your query:

  • Experiment ID: You can obtain this from the URL of the Experiment overview page. For example, if your URL looks like https://console.firebase.google.com/project/my_firebase_project/config/experiment/results/25, the experiment ID is 25.
  • Google Analytics property ID: This is your 9-digit Google Analytics property ID. You can find this within Google Analytics; it also appears in BigQuery when you expand your project name to show the name of your Google Analytics event table (project_name.analytics_000000000.events).
  • Experiment date: To compose a faster and more efficient query, it's good practice to limit your queries to the Google Analytics daily event table partitions that contain your experiment data—tables identified with a YYYYMMDD suffix. So, if your experiment ran from February 2, 2024 through May 2, 2024, you'd specify a _TABLE_SUFFIX between '20240202' AND '20240502'. For an example, see Select a specific experiment's values.
  • Event names: Typically, these correspond with your goal metrics that you configured in the experiment. For example, in_app_purchase events, ad_impression, or user_retention events.

After you gather the information you need to generate your query:

  1. Open BigQuery in the Google Cloud console.
  2. Select your project, then select Create SQL query.
  3. Add your query. For example queries to run, see Explore example queries.
  4. Click Run.

Query experiment data using the Firebase console's auto-generated query

If you're using the Blaze plan, the Experiment overview page provides a sample query that returns the experiment name, variants, event names, and the number of events for the experiment you're viewing.

To obtain and run the auto-generated query:

  1. From the Firebase console, open A/B Testing and select the A/B Testing experiment you want to query to open the Experiment overview.
  2. From the Options menu, beneath BigQuery integration, select Query experiment data. This opens your project in BigQuery within the Google Cloud console console and provides a basic query you can use to query your experiment data.

The following example shows a generated query for an experiment with three variants (including the baseline) named "Winter welcome experiment." It returns the active experiment name, variant name, unique event, and event count for each event. Note that the query builder doesn't specify your project name in the table name, as it opens directly within your project.

  /*
    This query is auto-generated by Firebase A/B Testing for your
    experiment "Winter welcome experiment".
    It demonstrates how you can get event counts for all Analytics
    events logged by each variant of this experiment's population.
  */
  SELECT
    'Winter welcome experiment' AS experimentName,
    CASE userProperty.value.string_value
      WHEN '0' THEN 'Baseline'
      WHEN '1' THEN 'Welcome message (1)'
      WHEN '2' THEN 'Welcome message (2)'
      END AS experimentVariant,
    event_name AS eventName,
    COUNT(*) AS count
  FROM
    `analytics_000000000.events_*`,
    UNNEST(user_properties) AS userProperty
  WHERE
    (_TABLE_SUFFIX BETWEEN '20240202' AND '20240502')
    AND userProperty.key = 'firebase_exp_25'
  GROUP BY
    experimentVariant, eventName

For additional query examples, proceed to Explore example queries.

Explore example queries

The following sections provide examples of queries you can use to extract A/B Testing experiment data from Google Analytics event tables.

Extract purchase and experiment standard deviation values from all experiments

You can use experiment results data to independently verify Firebase A/B Testing results. The following BigQuery SQL statement extracts experiment variants, the number of unique users in each variant, and sums total revenue from in_app_purchase and ecommerce_purchase events, and standard deviations for all experiments within the time range specified as the _TABLE_SUFFIX begin and end dates. You can use the data you obtain from this query with a statistical significance generator for one-tailed t-tests to verify that the results Firebase provides match your own analysis.

For more information about how A/B Testing calculates inference, see Interpret test results.

  /*
    This query returns all experiment variants, number of unique users,
    the average USD spent per user, and the standard deviation for all
    experiments within the date range specified for _TABLE_SUFFIX.
  */
  SELECT
    experimentNumber,
    experimentVariant,
    COUNT(*) AS unique_users,
    AVG(usd_value) AS usd_value_per_user,
    STDDEV(usd_value) AS std_dev
  FROM
    (
      SELECT
        userProperty.key AS experimentNumber,
        userProperty.value.string_value AS experimentVariant,
        user_pseudo_id,
        SUM(
          CASE
            WHEN event_name IN ('in_app_purchase', 'ecommerce_purchase')
              THEN event_value_in_usd
            ELSE 0
            END) AS usd_value
      FROM `PROJECT_NAME.analytics_ANALYTICS_ID.events_*`
      CROSS JOIN UNNEST(user_properties) AS userProperty
      WHERE
        userProperty.key LIKE 'firebase_exp_%'
        AND event_name IN ('in_app_purchase', 'ecommerce_purchase')
        AND (_TABLE_SUFFIX BETWEEN 'YYYYMMDD' AND 'YYYMMDD')
      GROUP BY 1, 2, 3
    )
  GROUP BY 1, 2
  ORDER BY 1, 2;

Select a specific experiment's values

The following example query illustrates how to obtain data for a specific experiment in BigQuery. This sample query returns the experiment name, variant names (including Baseline), event names, and event counts.

  SELECT
    'EXPERIMENT_NAME' AS experimentName,
    CASE userProperty.value.string_value
      WHEN '0' THEN 'Baseline'
      WHEN '1' THEN 'VARIANT_1_NAME'
      WHEN '2' THEN 'VARIANT_2_NAME'
      END AS experimentVariant,
    event_name AS eventName,
    COUNT(*) AS count
  FROM
    `analytics_ANALYTICS_PROPERTY.events_*`,
    UNNEST(user_properties) AS userProperty
  WHERE
    (_TABLE_SUFFIX BETWEEN 'YYYMMDD' AND 'YYYMMDD')
    AND userProperty.key = 'firebase_exp_EXPERIMENT_NUMBER'
  GROUP BY
    experimentVariant, eventName

Limits

A/B Testing is limited to 300 total experiments, 24 running experiments, and 24 draft experiments. These limits are shared with Remote Config rollouts. For example, if you have two running rollouts, and three running experiments, you can have up to 19 additional rollouts or experiments.

  • If you reach the 300 total experiment limit or the 24 draft experiment limit, you must delete an existing experiment before creating a new one.

  • If you reach the 24 running experiment and rollout limit, you must stop a running experiment or rollout before starting a new one.

An experiment can have a maximum of 8 variants (including the baseline) and up to 25 parameters for each variant. An experiment can have a size up to around 200 KiB. This includes variant names, variant parameters, and other configuration metadata.