How to Calculate Minimum Sample Size using Google Sheets

How to Calculate Minimum Sample Size in Google Sheets

Statistical rigor starts with knowing how many people you need in your test. You do not need to be a data scientist, just use Google Sheets and a few core inputs to calculate your minimum sample size for any A/B test. Here is a step-by-step breakdown you can embed in your process.

Step 1: Set Up Your Inputs

In a new Google Sheet, start by defining the following values in separate cells:

Step 2: Apply the Sample Size Formula

In cell A6, paste the formula below to calculate the minimum number of users required per group (A and B):
=2*((NORMSINV(1-A5/2)+NORMSINV(A6))^2)*A3*(1-A3)/(A4^2)
This formula uses the standard normal distribution to estimate the sample size needed to detect the specified minimum detectable effect with your desired confidence and power.

Step 3: Round It Off

In cell A7, round the number up to a whole number using:
=ROUNDUP(A6,0)

Step 4 (Optional): Calculate Total Sample Size

If you want the total required sample size across both variants, use this in cell A8:
=A7*2
If you did it right, your chart should look kind of like this:

Why This Matters

Testing without a valid sample size risks chasing false positives or missing real opportunities. This method gives you a clear, repeatable way to ensure your tests, CRO or otherwise, are statistically grounded without needing advanced software or a stats degree.

Pro tip: If your traffic volume is limited, use the output to set realistic expectations for test duration or reframe the test as directional rather than definitive.

Testing Without Rigor Is Just Noise

Running A/B tests without calculating statistical significance is like flipping a coin and calling it strategy. If you want your wins to hold up and your decisions to scale you need a clear, consistent approach to validating your results.

This method using Google Sheets gives you that clarity. No need for expensive platforms or complex analytics stacks. Just a few basic inputs and a calculator anyone can use. That is the difference between casual testing and true KPI optimization.

The best part? Once this process becomes muscle memory, your team stops asking “Did it win?” and starts asking “Does the data hold up?” and that is when real growth begins.

Other Calculations Worth Adding

Once you have nailed down minimum sample size, there are a few other calculations that can seriously improve the reliability and sophistication of your digital marketing and web testing strategy. One of the most useful is expected lift range: estimating the range of improvement you are likely to see from a variant based on your test design. This gives you better prioritization logic and helps align stakeholders around what “success” could realistically look like.

You should also calculate your test duration based on your daily sample volume. Once you know your minimum required sample size, you can divide that by your average daily traffic to estimate how many business days it will take to reach significance. That keeps your testing cadence rooted in reality and helps prevent premature conclusions that undercut your results.

Another critical metric is your P-value: the probability that your observed results could have occurred by random chance. A lower P-value means more confidence in your result, and most testing professionals use 0.05 as a threshold for statistical significance. Even if your A/B testing tool does this automatically, knowing how to interpret P-values gives you far more control over decision-making.

For more advanced programs, you can also explore confidence intervals and minimum detectable effect sensitivity. These metrics help clarify the precision of your result and ensure that you are testing changes big enough to matter. Whether you calculate them in Google Sheets or layer them in using a third-party tool, these deeper metrics help ensure your experiments are both efficient and conclusive.

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