Whether you are releasing new functionality or running an experiment, Split is constantly analyzing the change in your customer metrics to determine whether the impact is statistically conclusive and not simply happening by chance.

By configuring your organization's statistical settings, you can set an organization-wide default significant threshold where you want Split to mark your metrics impact as significant, and the default power threshold where you want Split to mark your metrics as having received enough samples.

## Significance threshold

Significance threshold is a representation of your organization's risk tolerance. Formally, the significance threshold is the probability of detecting a false positive.

A commonly used value for the significance threshold is 0.05 (5%), which means that every time you do an experiment, there is a 5% chance of detecting a statistically significant impact even if there is no difference between the treatments. In statistical terms, the significance threshold is equivalent to alpha (α).

## Power threshold

Power measures an experiment's ability to detect an effect, if possible. Formally, the power of an experiment is the probability of rejecting a false null hypothesis.

A commonly used value for statistical power is 80%, which means that the experiment has an 80% chance of detecting a difference if one exists. Assuming all else is equal, a higher power requires more samples to achieve a conclusive impact. In statistical terms, the power threshold is equivalent to 1 - β.

## Experimental review period

The experimental review period represents a period of time where a typical customer visits the product and completes the activities relevant to your metrics. For instance, you may have different customer behavior patterns during the course of the week or on the weekends (set a seven day period), or have a 30-day sales cycle (set a four week period).

A commonly used value for experimental review period is at least 14 days to account for weekend and weekly behavior of customers. Adjust the review period to the most appropriate option for your business, between 7 and 30 days.

## Recommendations and trade-offs

Be aware of the trade-offs associated with changing the statistical settings for your organization. In general, a higher significance and power threshold increases the number of samples required to achieve significance. Lowering these settings decreases the number of samples and the amount of time needed to declare significance, but may also increase the chance that some of the results are false positives.

As best practice, we recommend setting your significance threshold to between 0.01 and 0.1 and your power threshold to between 80% and 95%. In addition, we recommend an experimental review period of at least 14 days to account for weekly use patterns.

## Change settings

Navigate to **Admin Settings** > **Statistical Settings**. After you adjust your settings, click **Save**.

##### Note

Changing your statistical settings instantly affects your entire organization and all analysis. If your experiment is showing metrics as having a statistically positive impact at a .05 significant threshold, and you change your significance threshold from 0.05 to 0.01, the next time you load your metrics impact page you may see that metrics are no longer marked as having a significant impact.

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