> ## Documentation Index
> Fetch the complete documentation index at: https://confidence.spotify.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Sample Size Calculator
> Understand how the sample size calculator works and what affects required sample size.
The sample size calculator is a tool that assists in planning the length and
size of an experiment. The tool calculates what sample size you need to
achieve the requested level of power given the set-up of the experiment. The
required sample size differs across metrics. The tool also displays the largest
required sample size across all metrics. Having a large enough sample size is
important to ensure that the experiment has enough sensitivity to detect
meaningful effects. For more about what affects the required sample size,
see the [power](./design/power) page.
In addition to the required sample size, the calculator estimates the expected
number of samples and the number of days needed to reach the required sample
size for each metric. A progress chart visualizes this information, making it
easier to plan the experiment's runtime.
The expected sample size, days needed, and progress charts are currently in
beta. If you don't see these features in your organization, contact
[experimentation-cs@spotify.com](mailto:experimentation-cs@spotify.com) to
request access.
The sample size calculator doesn't take audience targeting into account. If
you are targeting a subset of the population, then the variance of the metrics
might be different for different subsets of the population. Some subsets might
have larger variance, which increases the required number of users to power a
certain [MDE/NIM](./design/effect-sizes), while others might have
smaller variances which could then decrease the required sample size for a
certain MDE/NIM.
Learn more about sample size calculations in the [the sample size calculation course](/docs/experiments/sample-size-calculator).
## Sample Size for New Metrics
When calculating the required sample size for an experiment, Confidence
looks at historical data for the metrics in the experiment.
There needs to be at least 14 days (plus the aggregation window and exposure
offsets) of historical data for the metric in order for Confidence to be able
to calculate the required sample size. For example, if you have an experiment
with a metric that has a 7-day aggregation window and a 7-day exposure offset,
you need at least 28 days of historical data. If there is not enough
historical data, Confidence can't calculate the required sample
size.
## Expected Sample Size and Days Needed Beta
The calculator uses the exposure source to estimate how many samples per day
the experiment can expect to receive. Based on this rate, it calculates:
* **Expected samples**: The estimated total samples by the end of the planned
runtime, based on historical exposure rates.
* **Days needed**: The estimated number of days to reach the required sample
size for each metric. The overall days needed shown in the widget header
is the maximum across all metrics.
When the expected samples exceed the required samples, the experiment is on
track to be sufficiently powered within its planned runtime. If the days
needed exceeds the planned runtime, consider extending the runtime or
reducing the required sample size.
Both expected samples and days needed are estimates based on historical
exposure from the selected exposure source. The actual exposure in a live
experiment may be lower if:
* The experiment uses audience targeting that excludes some users who were
included in the historical data.
* The flag has other rules that route some traffic away from the experiment.
Consider these factors when planning the experiment's runtime.
## Progress Chart Beta
The progress chart shows the estimated progress toward the required sample
size for each metric over time. The Y-axis displays the percentage of the
required sample size that the experiment has accumulated, and the X-axis
shows the number of days after the experiment starts. A horizontal line at
100% marks the required sample size target.
A vertical dashed line indicates the current planned runtime. Lines that
extend beyond this point appear as dashed projections based on the estimated
daily sample rate.
You can expand individual metric rows to view a per-metric chart. The
per-metric chart shows the absolute sample size on the Y-axis instead of a
percentage, with separate lines for the expected and required sample sizes.
## Exposure Source
The required sample size calculation consists of three parts:
* Obtaining an exposure source
* Using the exposure source to calculate the mean and variance of the metrics
* Calculating the required sample size for each metric based on the mean and variance
The exposure source is the source of the data used to calculate the mean and variance of the metrics. It can be one of the following:
* **Past assignments**: Use all existing assignments available in your assignment table, or filter these on assignments from specific flags to only include a cohort of users similar to those in your upcoming experiment.
- **Previous experiments**: Use exposure from previous experiments as an exposure source.
## Related Resources
Step-by-step sample size guide
Understand statistical power
Configure MDE and NIM settings
Configure alpha and power