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Creating An Experiment

How about we start creating some experiments? At the top of the dashboard, you will find the New Experiment button. Here you will be able to fine tune, edit and start your experiment.


The Basics step is where you will set some of the most important values for your experiment. The experiment name, the traffic allocation and the analysis type.

Experiment Name

The experiment name can be anything that you want! As it will be used in your code, we recommend using a keyword-like name with underscores. For example, large_homepage_carousel.

Traffic Allocation

The traffic allocation is the total amount of audience who will experience the experiment, including the control group. Usually, this is kept at 100% unless the change is considered dangerous. In which case, you could start it at 10%, move it up to 50% and eventually to 100% as you grow more confident in the change.

Type of Experiment

Coming Soon


The variants section is where you will choose your amount of variants, the percentage of traffic that each variant will have, your variant names and set any variant variables which can be used in your code.

Variant Split Manager

The Variant Split Manager is where you can select your number of variants and the split percentage across them.

You can have up to 4 variants at any one time and your split percentage can be whatever you want. But note, the split percentage cannot be changed after an experiment has started running without biasing the data. If you wish to ramp up an experiment, it's better to use the traffic allocation option instead.


The more variants you have, the less traffic each variant will be exposed to and the longer it will take for the experiment to finish.

Variant Variables

Variant variables can be used to automate your experiments. If you, for example, use a configuration file, you can pass a variant variable here and have it overwrite your code. See treatment variables in the SDK Documentation for more information.


Generally, we do not recommend changing variables on your control variant (Variant A).

Variant Screenshots

Screenshots can be added to each variant in an experiment, to help in identifying any changes to your UI during the experiment.


The audiences step is where you will control who can and cannot be a part of your experiment as well as the parameters which are used to define who sees what variant.

Tracking Unit

The experiment's tracking unit is the unique identifier that will be used to assign the user to a variant.

If the experiment is to be run across multiple platforms (Such as iOS, Android, Web, Email etc.), then this unit should be available on all of them. Most likely, the user_id of the authenticated user.

Conversely, if you wish for a user to experience different variants depending on their device, you could use their device_id as a unit.

Even further, to avoid experiment biases from users talking to eachother about their experience, companies such as LinkedIn, group their users into clusters and would use a cluster_id as their tracking unit.

You can create new tracking units in your dashboard settings.


Applications are the places where this experiment will be run. These could be "android", "ios" and "web", for example, and are also created in your dashboard settings.

Targeting Audience

Here you can define a specific audience who will experience the experiment. For example, you could decide to only test the experiment on British users. In this case you might set the filter group to:

These parameters can then be passed to the SDK in your code using context attributes.

Audience Enforced

When audience enforced is on, only users in the filter groups will be able to participate in the experiment. Others will be shown the control variant, but their data will not be tracked.

When audience enforced is off, any user can participate in the experiment, but the web console may warn you that your experiment is being shown to the wrong people. This helps you and your developers to remember to add the appropriate checks in your code when a decision has been made on the experiment. For example, to only show the tested variant to British users.


Metrics represent the data that is affected by your experiment.

Your Primary Metric is the statistic that you will use to make a decision on your experiment.

Secondary Metrics are not used to make a decision, but they may be affected by it. For example, if your Primary Metric is revenue_per_user it could go up because there are more customers making purchases or it could go up because customers are buying more expensive items. In this case, it might be useful to have users_that_convert and number_of_purchases as secondary metrics.

Guardrail Metrics are for giving you warnings about other parts of your business from the the Primary metric. If your new feature increases a user's time_on_page, but also increases your page_load_time significantly - the data may be showing you a false positive for that experiment.

Exploratory Metrics are the last of the metric types and are simply used for curiosity. They have no impact on your decision making process, but they might be interesting to look at the data of anyway.


Selecting any of the above metric types allows for them to be easily visualised on the experiment dashboard, but more metrics can always be selected at the bottom of the experiment details page.


The analysis step allows you to control statistical details of your experiment. The fields in this step will change depending on your selected type of analysis.

Type of Analysis

Here, you can choose which type of analysis your experiment will use. ABsmartly supports both fixed horizon and group sequential experiments:

Fixed HorizonGroup Sequential
Fixed horizon experiments have a predetermined end date, based on the sample size needed to make a decision. With this type of experiment, you will only make a decision once the sample size has been reached.Group sequential experiments have multiple check-in points, at each point you can make the decision to continue the experiment, or to terminate it early.

Error Control

In this section, you can choose your confidence and power levels for your experiment. The default values are usually fine, but if you wish to do some tweaking, this is the place to do it.


Increasing these values will cause your experiment to take longer to complete.

Monitoring (Sequential Only)

If you have selected Group Sequential as your experiment type, you will see the monitoring section here.

Number of Analyses

This is where you will be able to configure the number of analyses (decision points) that you will make before the experiment's final decision.

Futility Type

Futility type is an important concept in group sequential tests. Imagine you're watching a race between two tortoises, A and B. If A is way ahead of B, you can confidently say that A is the faster tortoise. Futility comes into play when A is only slightly ahead or even behind B. In such a case, it's not worth continuing the race, because it's unlikely that A will eventually win by a significant margin.

Futility types can be classified as either binding or non-binding.

Binding futility is like a strict rulebook that you must follow when deciding to stop a test early. If the test reaches a pre-determined futility boundary, you must stop it, no exceptions. This helps maintain control over the experiment and ensures that resources are not wasted on a test that's unlikely to yield meaningful results.

Non-binding futility, on the other hand, is more like a friendly suggestion. We alert you when the test might be futile, but you are not obligated to stop it. You can choose to continue if you believe there's still a chance for a meaningful difference to emerge. Non-binding futility offers more flexibility, but it can also increase the risk of making incorrect decisions if you choose to ignore the futility signal.

Sample Size Calculation

This is the section where you will determine how long the experiment will run for. The Web Console provides two ways of doing this.


The basic section allows you to simply select your Minimum Detectable Effect.

The minimum detectable effect (MDE) is the smallest difference in performance between the variations that an A/B test can reliably detect, given its sample size and statistical power. MDE helps determine whether the test is sensitive enough to identify meaningful differences in the results.

To calculate the MDE of your experiment, you can use the Power Calculator.


The lower your MDE, the higher the required sample size will be, and the longer the experiment will take.


In the advanced section, you can input some details about your selected Primary Metric and have us calculate the MDE for you based on how long you want the experiment to take.

Estimate Minimum Detectable Effect

If you choose to estimate the MDE, the values on the top of the table will be various power levels, including the one you selected in the Error Control section. The values on the left side of the table represent the number of participants per variant that will be required to complete the experiment. The rest of the data are MDEs for you to choose from to satisfy your needs for the experiment.


These sample sizes are estimates and may change slightly, depending on the data that comes in.

Estimate Maximum Experiment Duration

If you choose to estimate the Maximum Experiment Duration, the values on the top of the table will still be various power levels, but now, the values on the left side of the table are MDEs. The rest of the table's values are the number of participants per variant that will be required to complete your experiment.


You can also select to see the amount of participants in amounts of time. You can then input your number of users per day, per week or per month and have the estimated runtime calculated for you.


The description step is where you can fill in details about your experiment that may help others to understand what you hope to achieve with it.


The metadata section allows you to select the owners of this experiment, assign it to a particular team and add tags to help with searching and filtering later on.


Tags can be named anything you like. Often it can be useful to prefix your tags with the meaning of that tag. Some examples of tags could be:

  • stack:backend
  • theme:urgency
  • location:navbar
  • psychological:trust

These allow for you to filter your experiments in the experiments list page, but they also give you an insight into why that experiment was tagged as such.


When creating a new experiment, the description section acts as your contract for the test. Allowing you to define for yourself and your team why you are running this experiment, what you hope for the result to be and what will be done after any of the experiment's possible outcomes.


Your hypothesis is the assumed answer to the question that the experiment is asking.

For example:

Based on the fact that the color red is one of the most visible colors in the color spectrum, we believe that red call-to-action buttons are more noticeable on screen than blue ones.


Your prediction states what will happen if your hypothesis proves to be true.

For example:

Changing our call-to-action buttons to red will cause a higher click-through-rate to our checkout page from our visitors.


Your experiment's purpose is the reason for why this experiment should take place. What customer or business needs are being addressed.

For example:

Optimizing the efficiency of our sales pipeline.

Implementation Details

In your implementation details, you can define the parameters of your experiment's implementation, like:

  • How long it will take to implement
  • What's the impact that we can expect from this implementation?
  • What is the minimum impact that we need to see in order to keep this experiment?

For example:

This implementation will be completed by 9/24/2023.

We can expect an increase of up to 25% in our homepage click-through-rate.

At a minimum, we should see a 5% increase if we are to keep the change.

Action Points

Action Points are a list of actions that will be taken depending on the experiment's outcome.

For example:

If the primary metric is significantly positive we will keep the experiment.

If the primary metric is significantly negative we will drop the experiment.

If the primary metric is inconclusive we will drop the experiment.


The review step is where you can go over the experiment you have created and check its details before starting or saving it.

Experiment Actions

When you are happy with your experiment, you have 4 possible actions that you can take on it:

  • Save it as a draft
  • Save it as ready
  • Start it in development mode
  • Start it

Saving a draft

Saving your experiment as a draft means that you can come back to this experiment, to give it some final tweaks. When saving as a draft, all fields will still be editable when coming back to this experiment.

Saving as ready

Saving you experiment as ready is similar to saving it as a draft, but it signifies that the experiment is ready to be started. You cannot save an experiment as ready with invalid settings.

Starting in development

Starting your experiment in development allows you to test your experiment in your development environments.

An experiment in development mode will not run on a production environment, and the control variant will always be shown. This allows for you to test the implementation of an experiment before deploying it to production.


Starting your experiment will activate it in all environments and start collecting data from your users.

Up Next

The dashboard and how to read your experimentation data!