What if I launch a study with no prescreening filters applied?

At Prolific our participant pool is made up of a wide range of participants from many different countries.

If you launch a study on Prolific with no prescreening filters applied then any of our participants will be able to access that study.

We have algorithms in place to fairly allocate study spaces, and once a study is distributed to participants, it will be completed on a first-come first-served basis. This means that we can't guarantee the demographics of the participants who will complete your study if you've chosen not to apply prescreening filters.

However, this is the perfect option if you don't mind who gets to complete your study and want to get responses as fast as possible!

What if I want to target specific participants?

If you want to reach participants with specific demographics, we have a wide range of prescreening filters that can be applied to studies. This will ensure that your study is only accessible to the specific populations that you're looking to target.

If our in-built prescreening filters don't quite meet your needs then you're always welcome to recruit a custom sample.

What if I want a more balanced or representative sample?

When setting up your study, you can use the 'balance sampled' option to ensure an equal distribution of male and female participants.

Sometimes, you may wish to receive a specific distribution of participants across demographics other than sex. The best way to do this is by following our guidance on demographic balancing. This will give you control over how you balance your sample.

If you're looking to recruit a sample that reflects the demographic distribution of a given population, our representative sample feature is currently available for UK and US populations, and US political. There's also our representative samples FAQ page if you want a more in-depth look at how this feature works!

What if I want more/less naive participants?

Prolific uses two main approaches for participant recruitment. The first is convenience sampling, where study spots are filled on a first-come, first-served basis. The second is our Naivety Distribution system, which ensures a fair distribution of studies among participants.

The Naivety Distribution system helps reduce bias by balancing experienced and newer participants. This improves data quality by preventing studies from being dominated by highly experienced participants.

You can adjust this system to allow more participants to access your study immediately. This is especially useful if your research requires participants to join simultaneously.

One way to adjust the system is by removing rate-limiting mechanisms—though this may result in more experienced participants joining your study. You can request these changes for your entire account or for specific studies.

This flexibility is particularly valuable for studies that need concurrent participation, such as multiplayer games or dyadic experiments. However, keep in mind that removing rate limits may increase the proportion of experienced participants in your study.

If you'd like to customize these settings, contact our support team to discuss options that best fit your research needs.

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